THE EFFECT OF ORGANIZATIONAL IDENTITY ON CONSUMER ACCEPTANCE OF FIRM PRACTICES
Financial technology firms (Fintechs) in the banking industry: "Is the consumer acceptance of analyzing consumer transactions in the mobile payments market higher for Fíntechs than for traditional incumbent Banks?
Master thesis
Student name: Tami el Hamdi Student number: 10505563 31 January 2017
Supervisor: Nathan Betancourt University of Amsterdam
Economics and Business Faculty Executive Master in Business Studies
2
Statement of Originality
This document is written by Student [fill out your Given name and your Surname] 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.
3
TABLE OF CONTENTS
ABSTRACT 4
INTRODUCTION 5
LITERATURE REVIEW 8
Retail Banks from an Industrial Organization perspective 8
Retail Banks from a resource based view 9
Organizational identity 11
Acceptance of firm practice 14
Organizational identity and the effect on consumer acceptance 16 Consumer perception of fit with the organizational identity 17
METHOD 19
Sample 19
Research instrument 20
Variables and their measurement 22
Data analysis 25
RESULTS 27
Preliminary analysis 27
Testing the model 36
Hypotheses 40
Conclusion 40
DISCUSSION 41
Summary 41
Theoretical and practical implications 45
Limitations and future research 47
REFERENCES 49
4
ABSTRACT
The banking industry is shifting as a result of technological developments. Financial technology firms (Fintechs) have entered the market and are competing with traditional Banks. Analyzing of online payments, in the mobile payments sector, to reveal consumer spending behavior has become an area of fierce competition for market share. It appears that for Banks this firm practice is received unfavorably by consumers while not for Fintechs. This study 1) proposes that Banks and Fintechs have different organizational identities and 2) tests if organizational identity can explain this difference in consumer acceptance level of a firm practice. A ‘between-person’ vignette survey was conducted to verify the proposed difference in the organizational identity of Banks and Fintechs. Manipulation between the two groups was applied to measure the difference in acceptance level of the firm practice for the two groups. The results show that there is no effect of the organizational identity on the level of consumer acceptance for the firm practice. However, when analyzing the firm specifics which constitute the organizational identity, a significant relationship can be found for the following firm specifics: 1) when the relationship with the consumer is based on innovative products, 2) customer centricity, 3) safekeeping of consumers’ money and 4) trust. The results indicate that it is not the organizational identity as a whole, but only certain firm specifics that are predictors of consumer acceptance.
Keywords: acceptance, banks, disruption, financial industry, fintechs, fit, innovation, mobile payments, organizational identity.
5
INTRODUCTION
The boundary of the banking industry has become very blurred as a result of the technological developments of the past decades (Alt et al, 2012). Recent shifts in the banking industry are threatening the historically stable customer base that traditional Banks have (Accenture 2014). Although many consumers have been with their current bank for the past decade or more, the customer relationship at traditional Banks is up for disruption. This disruption, in the form of new market entrants, is a growing factor in the banking industry. Research by Gartner, Inc. reveals that technology will account for a network of about 25 billion mobile devices by 2020 with a nearly €2 trillion global economic benefit. As a result of this huge network, industries are shifting as consumers and their data become more accessible. The underlying value of most businesses who reach consumers online is based on the transfer of data. Technology companies like Google and Facebook have experience and knowledge on how to use data and solve big data problems. They also have a technology driven business model and this is one of their key strengths. The financial sector relies heavily on gathering and analyzing data, therefore it is not hard to imagine that technology companies are disrupting the financial services industry (McKinsey, 2014b). According to McKinsey (2014b), specifically the mobile payment sector has become an area with fierce competition for market share between traditional Banks and financial technology firms (Fintechs). The competition is mainly on consumer payments data. Access to and analyzing of transactions (data mining) can uncover powerful insights into customer needs, preferences and behaviors and therefore the consumer payment sector has become a hot spot for digital innovation (McKinsey, 2014b). However, the first signals from the public in the Netherlands show that consumers are not accepting the firm practice of ING Bank to analyze online payments to reveal consumer spending behavior (Bloomberg.com, 2014). Whereas technology firms such as Apple Pay and Google Pay already offer similar firm practices (Urban Airship, 2016). The negative response in the media has led to interference by
6
the government which proposed a law prohibiting the data mining of consumer spending data by Banks (Financieel Dagblad, 2016). This leads to the question, why do the consumer acceptance levels differ for similar firm practices? The firm practices are similar, however the offering firms, Banks and Fintechs are different (Alt et al, 2012). How are Fintechs different from Banks and how are they perceived by consumers? The answer could be found by looking closer at the organizational identity of the firm.
The concept of organizational identity is specified as the central and enduring attributes of an organization that distinguishes it from other organizations (Albert and Whetten, 1985). An organization’s identity constitutes consumers knowledge structures about a company (Bhattacharya et al, 2003). Therefore consumers are aware of the identity of a firm and consequently consumers could have different perceptions of the identity of Fintechs as compared with Banks. This raises the question if Banks and Fintechs have certain identities that differ from each other? If true, what is the effect of a perceived difference in identities between Banks and Fintechs by consumers on the acceptance level of the firm practice? Brown and Dacin (1997) argue that the consumer perception of the identity is important as consumers infer information and expectations from this perception. Therefore, expectations that are inferred from the identity could have an effect on consumer acceptance of certain firm practices.
Looking at the ING Bank experience as reported in the media (Bloomberg.com, 2014), the consumer acceptance of the firm practice seems to be higher for Fintechs than for Banks. This suggests that Fintechs could have an advantage over Banks in the mobile payment sector. Therefore, Banks are facing the challenge of consumer acceptance of data mining of their online payments. It is important for Banks to receive the consumer acceptance to be able to have and sustain their currently threatened competitive position as a result of the shifts in the Banking industry. The acceptance of data mining of consumer spending behavior is key for
7
both Banks and Fintechs as both are competing for market share in the mobile payment market. Examining the effect of organizational identity on the firm practice could improve understanding of how organizational identity, which is considered to be a resource of the firm, can contribute to a competitive advantage of the firm. As consumers have perceptions of firms and infer information and expectations from this perception, consumers could have a perception of the fit of the firm practice and the organizational identity of the firm.
This paper therefore examines 1) if the consumer acceptance of data mining of consumer online payments is higher for Fintechs than for Banks and 2) if this can be explained by the organizational entity of the firms. The theory will be examined from a strategy and consumer behavior perspective. The strategy perspective will underline how a firm practice could benefit from the organizational identity of the firm and if this subsequently could impact the consumer acceptance level of firm practices regarding data mining of online payments. This will contribute to the research on organizational identity and will provide more insight in how the organizational identities of Banks and Fintechs are different and why this can influence consumer acceptance and consequently be a source of competitive advantage. The consumer behavior perspective will examine whether consumer perceptions are a component of consumer acceptance of firm practices. Additionally the consumer behavior perspective will also be used to examine the perceived fit between the firm practice and the organizational identity. Organizational identity is therefore the independent variable and the level of consumer acceptance is the dependent variable. Fit between the perceived organizational identity and the firm practice is suggested to have a positive moderating effect on the level of Acceptance. This study aims to provide an understanding on the effect of organizational identity on consumer acceptance of firm. If the organizational identity is built or altered in such a way that it enables the firm to seize advantage in the form of higher consumer acceptance levels for firm practices, than this could improve the firm’s competitive position in the industry.
8
LITERATURE REVIEW
Retail Banks from an Industrial Organization perspective
The traditional business model of a retail bank is to lend money to retail customers and charge interest over that loan. The funds are mainly sourced from deposits, but also other sources of funds are possible. The bank profits from the difference in the interest that it charges on its lending activities and the interest that it pays on the deposits (Adams et al 2015). The analysis of the industry in the present day looks very different compared to a decade ago. This is mainly a result of the digitization of the banking industry and the increased power of the customers (Accenture, 2014). The power of the customers has increased significantly as a result of the internet which has eased and reduced the effort for consumers to compare Banks along with the rates offered at various Banks. Additionally as a result of the internet, customers are now within closer reach of Banks and therefore can switch easily to other Banks and have the option to bank online for a lower price.
The changes in the banking industry are even more obvious when looking at the threat of new entrants. Boundaries are key to the analysis of new entrants. Therefore firstly we examine the boundaries of the banking industry after which we discuss how the boundaries have become blurred as a result of technology developments. Banks hold very large sums of consumer funds which needs to be safeguarded, this is where banking regulation comes in place (Adam and Levitin, 2015). Banking regulations originate from concerns over the ability of bank depositors to monitor the risks originating on the lending side and from concerns over the stability of the banking system in the case of a bank crisis. Therefore regulatory and capital requirements of starting a new bank are high (Adam and Levitin, 2015). Another barrier of entry for the banking industry is trust. Because the industry deals with other people's money and financial information, new traditional Banks find it difficult to start up (Raija et al). Technology developments have loosened the entry barriers.
9
Other firms are able to enter the banking industry without being an actual bank or having a bank license as a result of technology developments (Accenture, 2014). These new entrants to the banking industry have specific technology based capabilities with which they are able to offer financial services and directly compete with incumbents (Mckinsey, 2014b). The new entrants in the Banking industry are called financial technology firms (Fintechs). The term Fintech is an abbreviation for financial technologies and covers all financial services and products where technology is applied. According to the Deutsche Bank ‘Fintech’ is the term that has now become established to describe the digitization of the financial sector. Fintech is a catchall used for advanced, mostly internet-based technologies in the financial sector. Fintechs are the firms that provide these innovative financial services, such as internet-based technologies in the e-commerce field, mobile payments or early-stage crowd-based financing of start-ups. The analysis of the industry shows that the entry barriers have loosened for the Banking industry and Fintechs are able to directly compete with incumbent Banks.
Retail Banks from a resource based view
The competition within the banking industry has increased as a result of the entrance of Fintechs to the banking industry. They way of competing for both firms are different; Fintechs firms have the image of being a successful innovator and a significant player in the digital world (Park et al, 2015, Dapp, 2015) which is based on an advice/personal relationship with the clients (Kotarba, 2016), while Banks are seen more solid, conservative and have a transactional relationship with their clients (Leveitim, 2015). The differences therefore seem to lie within the firm, for that reason the differences between Banks and Fintechs will be further examined from a resource based view.
Dierickx and Cool (1989), and Prahalad and Hamel (1990) built resource-based theory around the internal competencies of firms. They argue that competitive advantage comes from
10
within the firm and can be achieved by having valuable and inimitable assets. According to Grant (1991), resources can be categorized as tangible, intangible and workforce based. Intangible resources include reputation, technology and human resources. Omar et al (2009) argues that a very important driver for reaching a sustainable competitive advantage are intangible resources. Barney (1991 argues that in order for a resource to have a competitive advantage, it must be associated with the following four attributes: 1) it must be valuable, in the sense that it can exploit opportunities and/or neutralize threats in a firm's environment, 2) it must be rare among a firm's current and potential competition, 3) it must be imperfectly imitable 4) and there cannot be strategically equivalent substitutes for this resource that are valuable but neither rare or imperfectly imitable. According to this view, the key to sustainable competitive advantage is the possession of value-producing, intangible resources and capabilities that are rare, valuable, non-substitutable, and inimitable (Whetten and Mckay, 2002). Intangible resources and capabilities are typical organizational competencies (Barney, 1986; Fombrun et al, 2000). Organizational identity is an intangible resource and Abrat et al (2012) argue that an organization’s identity meets the four attributes of Barney (1991). Organizational identity can therefore be seen as a valuable intangible asset from a resource based perspective.
The literature on organizational identity has grown through calls for links between identity and strategy (Whetten and Godfrey, 1998) and includes the argument that identity drives strategy (Corley, 2004). The banking industry provides a unique opportunity to examine if organizational identities are able to provide competitive advantage to various players in the industry. Currently both incumbent Banks and Fintechs are active in the banking industry. It is important to understand what their organizational identities are and how their organizational identity could contribute to their competitive advantage.
11
Organizational identity
The concept of organizational identity is specified as the central and enduring attributes of an organization that distinguish it from other organizations (Albert and Whetten, 1985). It represents central perceptions and beliefs about what distinguishes an organization from others (Hatch and Schultz, 2002). Albert and Whetten (1985) conceptualized organizational identity as those characteristics collectively understood by an organization's members to be central, distinctive, and enduring. It specifies that organizations are unique among other groups as social actors (Whetten and Mackey, 2002). Viewed from this perspective, the definitional elements proposed by Albert and Whetten (1985) (central, enduring, and distinctive), could be seen as functional requirements of an organization’s’ self-definition. Using their language, organizational identity consists of those identity claims used by organizations for purposes of specifying what is most central to the organization that is also most enduring and or/most distinctive. The more that an organization’s unique source of competitive advantage is an outcome of its core identity claims, the more likely it is that this intangible resource or capability can be legitimately characterized as an organization-specific asset (Whetten and Mckay, 2002). Additionally, organizational identity and the resource-based theory both assume heterogeneity, and this makes them fertile avenues to explore for sources of competitive advantage via difficult to imitate and socially complex resources (Whetten & Corley,1998).
Looking at the retail banking industry, a division can be made between the organizational identity of traditional Banks and the Fintechs based on their firm attributes. Their economic activities overlap, but their organizational identities are different following the organizational construct of Whetten. The central attribute is the one that has changed the history of the company; if this attribute was missing, the history would have been different. For traditional Banks their central attribute is having a bank license and being responsible for holding and safely keeping the savings of consumers. All traditional Banks have this in
12
common. Fintechs on the other hand have a different central attribute, as their existence has emerged from the technology area, and they would be non-existing without the internet. Enduring attributes are deeply rooted in the organization. The enduring attribute of a traditional bank is that it keeps accounts of consumers and lends money to consumers, which has led to an important role in society for banks. It can manage these cash flows taking into account the risks associated with it. Technology firms have the enduring attribute of having knowledge on data and the internet. This allows them to innovate and develop services and products which are technology based.
Distinguishing attributes are used by organizations to separate themselves from other similar organizations. Currently the literature has not formed an opinion on the distinguishing firm specifics for Banks and Fintechs that form their organizational identity. Existing literature has presented specific firm specifics for Banks and Fintechs without specifying that this is at the core of their identity. To test the different corporate identities several firm specifics which together form a central, distinctive and enduring attribute are assumed and tested in the survey. The assumptions for firm specifics for respectively Banks and Fintechs are based on descriptions, in literature and are listed in table 1.
13
Table 1 - Organizational Identity firm specifics in literature
Linking description in literature Source
1 Safety_ purpose Banking is based on two fundamentally irreconcilable functions: safekeeping of deposits and relending of deposits' (abstract, p.1)
Adam J. Levitin (2015) Safe Banking
2 Trust In the banking sector, consumer trust in the entire company is of utmost importance' (p. 554)
Raija Anneli Järvinen (2014) Consumer trust in banking relationships in Europe 3 Role_Society Banks play a major role in all the economic and financial
activities in modern society.' (from abstract)
P BuČka, M BuČKovÁ (2011) The role of banks in the world
of finance 4 Norms Responding to public and political criticism of ethical
decision making in the financial sector, the Banker’s Oath was introduced in Holland in 2012. (from abstract, p.1)
Wybe T. Popma (2017) CSR and Banking Morals: On the Introduction of the Dutch Banker’s Oath
5 Innovative_firm FinTech is an innovative technology through IT platform and big data' (from abstract)
Park, Jung-Oh; Jin, Byung-Wook (2015) A Study on Authentication Method for Secure Payment in Fintech Environment 6 Relationship_
innovative
Additionally, a view on the FinTech industry is presented, highlighting areas where traditional financial institutions are losing market share to technology-savvy and socially oriented new ventures with exceptional CRM capabilities . (from abstract)
Marcin Kotarba (2016) New Factors Inducing Changes in the Retail Banking Customer Relationship Management (CRM) and Their Exploration by the Fintech Industry
7 Data_knowledge They can all utilise advanced key technologies, such as algorithm and data-based, cognitive, self-learning systems , in order to retain more customers. The transformation into digital and open platforms therefore represents an attractive and lucrative solution, even though the reform of existing structures requires far more effort than starting anew from scratch.' (p.7)
Thomas F. Dapp (2015) Fintech reloaded – Traditional banks as digital
ecosystems
With proven walled garden strategies into the future
8 Consumer_centricity The research of KPMG and H2 Ventures (KPMG and H2 Ventures, 2015) shows that leading FinTechs are already exploring all elements of the traditional CRM value chain of financial institutions. Their innovative business models and disruptive nature of their technologies stem from a better understanding of client needs in the changing world. (p. 71)
Marcin Kotarba (2016) New Factors Inducing Changes in the Retail Banking Customer Relationship Management (CRM) and Their Exploration by the Fintech Industry
Organizational_identity firm specifics
14
Acceptance of firm practice
The banking industry has seen several waves of innovations for services delivery that have changed the ways that customers and Banks interact. Progresses in information communication and technology have played an important role in initiating, driving and shaping these innovations (Hatzakis et al. 2010). In the recent past mobile payments, high-frequency trading (HFT), Bitcoin, and crowd funding have been shaping the new high-tech landscape of financial services in the late 2000s up to the present (Aldridge 2013). Mobile phone manufactures, telecom operators, payment service providers, software companies, and technology start-ups all are entering the payment market (Gartner, 2015 and McKinsey, 2014b). Payments have become one of society’s most innovative and dynamic sectors, with fierce competition for market share. When looking specifically into the mobile payment industry, after the market downturn years of 2008–2011, firms such as Google, PayPal, and Apple Pay have brought mobile payment technology and service innovation to the marketplace (McKinsey, 2014b). A formal definition of mobile payments is any payment in which some kind of a mobile device is used to initiate, authorize and confirm an exchange of financial value in return for goods and services (Karnouskos 2004). Currently, transactional data remains one of the keys areas of focus for financial institutions. Analyzing transactions can uncover powerful insights into customer needs, preferences and behaviors and therefore consumer payments have become a hot spot for digital innovation (McKinsey 2014b).
However, transactional data represent only one type of information asset. Other important types of information assets include both structured data (demographic profiles, website browsing activity) and unstructured data (call center logs, correspondence). Social media and search engines represent an important source of that data. Social media also offers many opportunities for developing personalized campaigns and offers. This research paper focuses on the firm practice of data mining of consumer mobile payments with the aim to
15
uncover powerful insights into customer needs, preferences and behaviors. Banks can integrate the other types of information with their existing information to develop a holistic view of their customers. Fintechs are keen on getting their hands on payment data as this can be integrated with their existing information to develop a holistic view of their customers as well. As the volume, velocity and variety of the internal and external data continues to increase, Banks need to equip their employees with tools and skills to glean powerful insights from the data to drive business forward. To be able to achieve this, it is important that consumers accept the firm practice of mobile payments with the aim to analyze transactions to uncover powerful insights into customer needs, preferences and behaviors.
Earlier literature sought to explore individual mobile banking acceptance relying on the consideration that it is a technical innovation (Al-Jabri & Sohail, 2012). Among several different models that have been proposed, five theoretical models predominated in the literature (Hoehle, Scornavacca, & Huff, 2012), until when Venkatesh et al. (2003) introduced the unified theory of acceptance and use of technology (UTAUT). These are the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975), technology acceptance model (TAM) (Davis, 1989), theory of planned behavior (TPB) (Ajzen, 1991), innovation diffusion theory (IDT) (Rogers, 1995) and theory of perceived risk (TPR) (Featherman & Pavlou, 2003). In 2003 Venkatesh et al. (2003) proposed the unified theory of acceptance and use of technology (UTAUT), introducing an improved model in 2012 (UTAUT2). All the above discussed models rely on the consideration that it is a technical innovation aimed at analyzing the adoption of the technology and the underlying factors influencing the adoption, rather than measuring the level of acceptance. This study does not have the intention to measure the adoption of the technology, as only users that already make use of mobile payment services are included. As, acceptance in this study is aimed at measuring the level of acceptance, none of the above mentioned models were used directly. Important predictors of acceptance form the earlier discussed models are
16
all related to the attractiveness of the service, the expected benefit of the service and the intention to use the service. Therefore these three constructs are used. The constructs and their source in literature are listed in table 2.
Table 2- Suggested items for construct acceptance in literature
Organizational identity and the effect on consumer acceptance
The theory on organizational identity suggests that Fintechs and Banks have different organizational identities when their central and enduring attributes distinguish them from each other. The assumption is made in this study that Banks and Fintechs have different organizational identities as summarized in table 1. Organizational identities constitute consumers knowledge structures about a company. These knowledge structures are corporate associations (Brown and Dacin 1997). They include the customers’ perceptions about relevant company characteristics (e.g., culture, climate, skills, values, competitive position, product offerings), as well as their reactions to the company (e.g., Dowling 1986). Literature in the field of consumer behavior suggests that consumers are aware of the identity of a firm and that consumers’ cognitive associations (i.e., corporate associations) of these company characteristics can be both a strategic asset (Dowling 1993) and a source of sustainable competitive advantage (Aaker 1996). This is further supported by Brown and Dacin (1997).
Linking description in literature Source 1 Expected benefit According to UTAUT, performance expectancy, effort expectancy, and social
influence are theorized to influence behavioral intention to use a technology, while behavioral intention and facilitating conditions determine technology use.Here, performance expectancy is defined as the degree to which using a technology will provide benefits to consumers in performing certain activities;
2 Attractiveness Second, from the perspective of effort expectancy, in organizational settings, employees assess time and effort in forming views about the overall effort associated with the acceptance and use of technologies. In a consumer technology use context, price is also an important factor as, unlike workplacetechnologies, consumers have to bear the costs associated with the purchase of devices and services.
3 Intention to use UTAUT and related models hinge on intentionality as a key underlying theoretical mechanism that drives behavior. Many, including detractors of this class of models, have argued that the inclusion of additional theoretical mechanisms is important. In a use, rather than initial acceptance,
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. Constructs of acceptance
17
Brown and Dacin (1997) argue that perceived corporate associations may be used by consumers in establishing a corporate context for accepting new products. As an identity comprises of consumers’ knowledge structures about a company and the same holds op for corporate associations, it is arguable that consumers have a perception of the identity. Brown and Dacin (1997) argue that the consumer perception of the identity is important as consumers infer information and expectations from this perception. Therefore, expectations that are inferred from the identity might affect their response on the firm practice in the form of the consumer acceptance level. The first hypothesis tests if these consumers’ knowledge structures are taken into account when accepting a practice offered by the firm. In other words, is the perceived organizational identity of the firm able to explain the difference in consumer acceptance level? The hypothesis tests if this acceptance is higher for Fintechs than for Banks in line with the current situation in the Banking industry.
H1) the consumer acceptance of data mining in the mobile payments sector is higher for technology companies (Fíntechs) than for traditional incumbent Banks
Consumer perception of fit with the organizational identity
Fit is a construct which has been researched in much of the sponsorship, branding, and endorsement literatures and are consistent with associative network theory. Associative learning is a mechanism by which links or relationships between concepts can be established (Klein, 1991, Martindale 1991). An associative network structure of memory consist of patterns of nodes (concepts) linked together (Anderson 1976,1983) In the context of the organizational identity and the firm practice, both the organization (either being Fintechs or Banks) and the mobile payment practice represent units connected to other units based on the association set for the organization and for the practice. For example in the branding literature, consumers are held to invoke the beliefs they have formed through past experience with the brand to make inferences about the quality of the extension (DelVecchio, 2000). In essence,
18
consumers use their experiences with the other products affiliated with the brand as a surrogate for experience with the new product (DelVecchio, 2000). Fit is important because it influences: (1) the relationship itself when perceived inconsistencies with prior expectations and information exist (Foreh and Grier, 2003, Meyers-Levy and Tybout, 1994) (2) The specific types of thoughts generated (e.g., low fit generates negative thoughts and low fit itself is considered negative) (Foreh and Grier, 2003); and (3) evaluations of the two objects (Johar and Pham, 1999, Sen and Bhattacharya, 2001 and Speed and Thompson, 2000). Specifically, high levels of perceived relatedness enhance consumer attitudes towards firms/brands because they view the actions of firms as appropriate (Aaker, 1990, Aaker and Keller, 1993, Mandler, 1982, Till and Busler, 2000).
The firm practice discussed in this paper relates to the use of consumer data by analyzing transactions which can uncover powerful insights into customer needs, preferences and behaviors. This is a firm practice of both the traditional Banks and the Fintechs. As the practices are similar but the organizational identity of the firms differs, the perceived fit of the firm practice with the organizational identity could therefore be an influencing factor of the level to which consumers are accepting the firm practice. The constructs used in this study to measure the level of perceived fit does not stem from existing literature as fit between organizational identity and firm practice have not been researched before. Therefore a scale of measurement has been developed based on the construct of ‘levels of perceived relatedness’.
H2) the perceived fit of the firm practice of data mining in the mobile payments market with organizational identity will positively influence the level of consumer acceptance
19
Figure 1 - The hypothesized relations into a conceptual model.
Organizational identity: the organizational identity of Banks or the organizational identity of Fintechs.
Consumer acceptance of firm practice: the consumer acceptance of data mining in the mobile payments sector
Fit: the perceived fit of the firm practice of data mining in the mobile payments market with the organizational identity
METHOD
Sample
This research aims to support the hypothesis that consumers respond differently to firm practices depending on the organizational identity of the firm. The research subject is focused on online firm practices in the Retail Banking industry in the Netherlands. The research is conducted in English. Two organizational identities are suggested to be present in the Retail Banking industry, which are the organizational identity of a traditional Bank and the organizational identity of a Fintech. The ‘firm practice’ subject to research is defined as: The bank or the Fintech, offers customers discounts for products/services offered by other companies, based on their spending patterns, as revealed through data mining. These spending patterns include mobile spending only. This requires that customers already use mobile banking. Therefore the first eligibility criterion for the study participation is that participants should already be using mobile banking. This requirement is achieved by starting the
Organizational Identity Fit Consumer acceptance of firm practice + H2 H1
20
questionnaire with the question whether respondents use online mobile banking as their primary way of banking. The second inclusion criterion consist of the organizational identities of the two firm types, Fintechs and traditional Banks, and the requirement that consumers should be aware of the existence of such companies. This requirement is met by explaining in in the questionnaire what is meant by Fintechs and by providing examples of Fintechs, the same is done for Banks. The third inclusion criterion, the firm practice which is subject to research, this firm practice is currently already offered by Fintechs (the two largest ones are Google Wallet and Apple Pay) but not yet by traditional Banks. Traditional Banks have the intention to introduce these firm practices as well. As the firm practice is not offered yet by traditional Banks in Europe, this research will focus on the intention of accepting the firm practice. Therefore, the participant is asked about their intention to use the firm practice. The assumption is made that because the sample excludes consumers that opted out to the use of online banking practices, privacy concerns related to the use of the service should be limited.
Research instrument
The data used for this research is collected by a survey that was distributed via social media channels (Facebook and Linkedin), this was done to be able to collect as much data as possible in a short period of time with very low costs. The research approach used to test these hypotheses is of quantitative nature which is needed to perform the regression analysis. The approach for the survey is a vignette study in the form of a paper people study which will address two organizational identities (Banks and Fintechs). In a paper people study respondents are presented with vignettes in written form and respondents are asked to make explicit decisions, judgments and choices or express behavioral preferences. This approach is chosen as it has been proven useful in other studies when control needs to be exercised over the independent variable (Cavanaugh & Fritzsche, 1985). This control over the independent variable is needed, as respondents need to provide answers without having in mind both the
21
Fintechs and Banks. If the comparison would be made in the survey, than respondents would be able to understand the purpose of the comparison and could potentially provide biased answers. The surveys used are identical but are addressing two different firm types, one being traditional retail Banks and one being Fintechs.
The survey consists of 3 sections. The first section asks about consumer’s perception of identity components in relation to either Fintechs or Banks. The second section presents a case which describes a firm practice which is identical in both surveys where one addresses Fintechs and the other survey Banks. The two surveys are randomly assigned to the respondents by the software program Qualtrics. The final section of the survey asks for demographic data of the respondents. The surveys are added as an appendix to this study.
A between-person design is used as part of the vignette approach, which means that each participant only takes part in one survey. This approach will create two groups of respondents which allows for comparison of the two groups. This type of design will help to show the consumers’ perception of the organizational identity and the fit of the organizational identity with the firm practice without the bias as previously mentioned. The effect of manipulation between groups will be useful in uncovering the associations that consumers have with organizational identities and their firm specifics. Therefore, it is important the consumer should not be defining their thoughts on their difference in perception during the survey, rather they should answer without having the ability in the survey to compare Fintechs and Banks. That is the main reason why a between-person design has been chosen as an approach.
To address the research aim, the participants are selected using a non-probability sampling method as it is difficult to identify members of the desired population. A snowball sampling technique is used by putting on an online survey link on Facebook and Linkedin to collect as many respondent as possible in a short time frame of two weeks. The scale of measurement is a slider scale with values 0 to 100. Where for all questions 0 represent a
22
negative opinion and 100 a positive opinion. No counter indicative questions are part of the questionnaire. The reason for making use of a slider scale is that it reduces the time a respondent has to spend on filling in the survey, which may increase the response rate. Additionally, the usage of a slider scale makes variables suitable for regression analysis due to its distribution levels of the respondents’ answers (0 to 100).
Variables and their measurement
The research is designed to test the applicability of the hypothesis using a questionnaire. No existing scale of measurement questions was used to measure the variables. The questions have been developed specifically for the purpose of this research. (See appendix 1 for the questionnaire). In this section the variables will be discussed and the way of measurement for each variable.
Dependent variable
Acceptance_FP: The firm practice discussed in this paper relates to the use of consumer data
by analyzing transactions which can uncover powerful insights into customer needs, preferences and behaviors. This is a firm practice that is proposed to be offered by both Banks and Fintechs. Consumer acceptance in this paper is defined as the expected intended use of the firm practice, the attractiveness of the firm practice and the expected benefit of the firm practice. The survey included three questions that measure the variable consumer acceptance
of the firm practice. The questions measure the level of attractiveness of the firm practice, to
what level the firm practice is expected to benefit the consumer and the level of intention the respondent has to use the firm practice service. Zero on the slider scale refers to the lowest level of agreeableness, and a score of 100, to the highest level of agreeableness. The outcome of the three questions are averaged in to one variable, which is Acceptance_FP.
23
Independent variables
Organizationa_ID: Albert and Whetten (1985) conceptualized organizational identity as those
characteristics collectively understood by the employees to be central, distinctive, and enduring. As an organizational identity comprises of consumer’s knowledge structures about a firm, it is arguable that consumers have a perception of this identity. It is argued that in the retail banking industry, the organizational identity of Banks and Fintechs are different. Organizational identity in this study is therefore a binary variable it can either be the organizational identity of a Bank (0) or a Fintech (1). The presence of two different identity groups in which each respondent was randomly allocated, are validated by eight questions. The questions measure which associations consumer have with certain firm specifics in relation with the company type. The expectation is that four questions will get a higher grade of association with Banks compared to Fintechs and that four questions will get a higher grade of association with Fintechs compared to Banks. An example of a question in the organizational identity section of the Fintech survey which measures the firm specific Trust is “The relationship with the Fintech is based on trust”. A score of 0 on the slider scale refers to the lowest level of agreeableness, and a score of 100, to the highest level of agreeableness. The identified firm specifics, the related survey questions and their expected relationship with the organizational identities are presented in table 3.
24
Table 3 - Organizational identity firm specifics definition
Fit_IDFP: Fit in this paper is defined as the perceived link between a firms practice and its
perceived organizational identity. The survey includes three questions that measure the variable perceived fit of the service and the organizational identity. The questions measure to what extent the respondent agrees to the idea that either Fintechs or Banks provide this service, if Fintechs/Banks are the right company to offer this service, and if the service with the perception that the respondent has of Fintechs/Banks. Zero on the slider scale refers to the lowest level of agreeableness, and a score of 100, to the highest level of agreeableness. The questions are analyzed on reliability and a factor analysis is performed to judge the measurement reliability.
Control variables. Although the respondents are randomized between Fintechs and Banks, the
results of the regression model are controlled for several demographical factors that may play a role in predicting the level of acceptance. We control for gender (male=0 and female=1), as there could be a difference in the general level of acceptance of firm practices between men and women. We control for age as it could be that the generation that the consumer is part of could have an impact on how they perceive the organizational identities of Fintechs and Banks as Fintechs are relatively new firms and depending on the age of the consumer, the perception
Questions in Bank survey to measure firm specifics
Questions in Fintech survey to measure firm specifics
Expected mean differences
1 Safety_ purpose Bank s keep consumer's money safe
Fintechs keep consumer's money safe
Significantly lower for Fintechs 2 Trust The relationship of Bank s with
the consumer is based on trust
The relationship of Fintechs with the consumer is based on trust
Significantly lower for Fintechs 3 Role_Society Bank s have an important
responsibility in society
Fintechs have an important responsibility in society
Significantly lower for Fintechs 4 Norms Bank s should highly value
norms and rules set by society
Fintechs should highly value norms and rules set by society
Significantly lower for Fintechs 5 Innovative firm Bank s are innovative
(innovative means developing or inventing new things)
Fintechs are innovative (innovative means developing or inventing new things)
Significantly hgher for Fintechs 6 Relationship_
innovative
The relationship of Bank s with the consumer is based on innovative products and services
The relationship of Fintechs with the consumer is based on innovative products and services
Significantly hgher for Fintechs
7 Data knowledge Bank s are experts on data and the internet
Fintechs are experts on data and the internet
Significantly hgher for Fintechs 8 Consumer
centricity
Bank s think that consumer's needs and wishes are important
Fintechs think that consumer's needs and wishes are important
Significantly hgher for Fintechs
Organizational_id firm specifics
25
of both identities might be different. We also control for the education level (High_school, MBO, HBO, Bachelor and Master) as the level of education might influence the level of knowledge that consumers have on what constitutes a Bank and what a Fintech. The final control variable is sector of employment (Government_sector, Banking_sector, Fintech_sector and all other sectors categorized as Other_sector) because respondents employed in the Banking sector, Government sector or the Fintech sector might have a biased opinion compared to the reference group which is the category ‘Other_sector’.
Data analysis
The results of the survey are analyzed using IBM SPSS software. Below a description of the analyzing techniques that are performed.
Statistical procedure preliminary analysis
The control variables exist of both interval data and categorical data. When analyzing the differences in the control variables between the two groups (Fintechs and Banks) the chi square is used for the categorical variables (education, job, gender) and for interval data an independent T test is performed (age). For all interval data a check is also performed on the normal distribution of the data. It was not necessary to recode counter-indicative items as no such items where present in the dataset. Dummy variables are created for the categorical control variables.
To determine the validity of the two organizational identity groups, Banks and Fintechs, a factor analysis is performed. Several well recognized criteria for the factorability of a correlation are used. A correlation analysis is performed to check for reasonable factorability. Secondly the Kayser-Meyer_Olkin measurement of adequacy is performed which should give the number of factors taken into account the Eigen Value of 1. Finally the communalities are checked to be all above .3. A principal component analysis is conducted because the primary
26
purpose is to identify the underling firm specifics that belong together. Additionally a Varimax rotation has been applied to check for a primary factor loadings over .4 and a cross-loading of more than .3. Additionally, for manipulation purposes an independent sample T-test is performed on the organizational identity variable, by means of the associated firm specifics. The Bonferoni correction was applied for multiple testing as a number of 8 variables were tested. This adjustment was required to reduce type I error. The adjusted p-value (0.05/8) is 0.00625 to consider mean differences significant. Also the assumption for equality of the variances between Fintech and Banks was inspected with Levene’s Test.
The survey outcomes of the three questions that belong to the dependent variable Acceptance_FP are interval continuous data. A factor analysis test is performed and checked against the condition of the required KMO test. The outcomes are, after testing for reliability and the performance of a factor analysis, averaged into one variable.
There are three questions that are expected to measure the level of perceived fit between the firm practice and organizational identity. To verify that these three questions measure a different construct than the three questions that measure Acceptance_FP, a factor analysis is performed including all six questions. The six questions should measure two constructs: three questions should measure the construct Acceptance_FP, and another three questions should measure the construct Fit_IDFP. Attractiveness, expected benefit and intention to use should form the construct Acceptance_FP. Idea, right company and perception of fit should form the construct Fit_IDFP.
Statistical procedure for testing the hypotheses
A hierarchical linear regression analysis is performed to test hypothesis 1 and 2. A correlation analysis is performed to explore relationships between variables. For the categorical variables the Spearman approach has been used and for the continuous variables the Pearson method has been used very high correlated items can be an indicator for multicollinearity. Absence of
27
multicollinearity is an assumption for performing a valid regression model. The normality and linearity assumption are tested for as well. After checking for the assumptions required for linear regression analysis, the regression analysis is performed. The regression analysis exists of several models. The first model tests for a significant impact coming from the control variables on the dependent variable acceptance. The second model adds the organizational identity variable to see the influence of organizational identity on acceptance after controlling for the control variables. Model three adds the moderator variable fit to the regression analysis to identify if fit increases or weakens the relationship between organizational identity and consumer acceptance. Model four tests if the interaction between fit and organizational identity has a significant impact on consumer acceptance.
A sensitivity analysis in the form of a linear regression analysis is also part of the results section, where the firm specifics are considered as predictors of the model instead of Acceptance_FP. This sensitivity analysis is performed as a manipulation test to check the external validity of the independent variable. The basis of this research is a vignette study where two groups are created depending on a random distribution of the Fintech survey and the Bank survey. Therefore, the independent variable is a result of the collective conscious or unconscious respondents’ belief of what consist an identity of a Fintech and what consist an identity of a Bank. The sensitivity analysis is performed to test if instead of taking the collective understanding of the organizational identity of the respondents, the outcome of the model will change if the firm specifics are considered as the independent variable.
RESULTS
Preliminary analysis
A total number of 273 respondents participated in the survey of which 60 did not complete the survey. The 60 incomplete surveys were excluded. Of the total 213 completed surveys, a
28
number of 30 did not meet the criterion of using online mobile banking as their primary way of banking and where therefore also excluded. A number of 11 respondents did not have a complete response and were excluded as well. This results in 172 complete filled in survey. The descriptive statistics show that there were a total number of 172 respondents which can be divided into 96 that filled in the questionnaire related to Banks and 76 related to Fintechs. The elimination of incomplete or invalid surveys has led to a difference in the distribution outcome of the two groups. The analysis of the control variables which are mainly demographic variables have shown not to be statistically different when both groups are compared, acknowledging that the respondent are still equally distributed over the two groups. Looking at gender, 66 respondents were female (38%) of which 39 (41%) took the Bank survey and 27 (36%) took the Fintech survey, not having a significant impact. The average age of the respondents was 34 and is very similar for both groups. Looking at educational level, the largest concentration is in respondents with a Masters background (69, 40%) and with a HBO degree (58, 32%), overall the level of education was not significantly different between the two groups. The largest part of the respondents are employed in a sector other than Banks (47, 27%), Fintechs (5, 3%) and Governments (24, 14%), resulting in 96 (56%) that fall into the other category. But this is also not significantly different. To conclude, groups show equal distribution of characteristics, depicted in Table 2.
29
Table 4 - Descriptive statistics
Organizational Identity
Firstly it was observed that all items related to the Fintech organizational identity correlated at least .3 with at least one other item, suggesting reasonable factorability (see table 5). This was not the case for the items suggested for the Bank where the 0.3 boundary was only met for items Safety_purpose and Trust, suggesting weaker factorability
Table 5 - Correlations among and descriptive statistics for Organizational_ID firm specifics
The explorative factor analysis has been performed without predefining the number of expected factors. The Kayser-Meyer-Olkin measure of sampling adequacy was (.696) above the commonly recommend value of .6. Finally, the communalities were all above .3, further
All Bank Fintech
Demographic variables Mean(SD) / N (%) Mean (SD) / N (%) Mean (SD) / N (%) P value
N 172 (100%) 96 (56%) 76 (44%) Gender (Female) 66 (38%) 39 (41%) 27 36%) 0,495 Age 34 (10) 35 (11) 34 (8) 0,421 Education 0.772 -High school 4 (2%) 3 (3%) 1 (1%) -MBO 23(13%) 14 (15%) 9 (12%) -HBO 69 (40%) 39 (41%) 30 (39%) -Bachelor 18 (11%) 8 (8%) 10 (13%) -Master 58 (33%) 32 (33%) 26 (34%) Job industry 0.100 -Government 24 (14%) 11 (12%) 13 (17%) -Bank 47 (27%) 21 (22%) 26 (34%) -Fintech 5 (3%) 4 (4%) 1 (1%) -Other 96 (56%) 60 (62%) 36 (47%) M (SD ) 1 2 3 4 5 6 7 8 1 Safety_ purpose 68.3 (21.0 -2 Trust 67.7 (24.7) ,307** -3 Role_Society 77.7 (20.6) ,258** ,201** -4 Norms 74.4 (21.2) 0,06 ,211** ,255** -5 Innovative firm 66.1 (21.4) -6 Relationship_ innovative 63 (21.6) ,643** -7 Data knowledge 60.3 (21.6) ,505** ,406** -8 Consumer centricity 56.6 (24.0) ,406** ,387** ,474**
-**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Firm specifics
30
confirming that each item shared some common variance with other items. Given these overall indicators, factor analysis was deemed to be suitable with all 8 items. The principal component analysis was used because the primary purpose was to identify the underling firm specifics that belong together. Initial Eigen Values indicated that the two factors explained 32.2% and 19.1% of the variance respectively. The PCA has been followed by a Varimax rotation analysis. Table 7 shows that that the factor analysis indeed confirms that the 8 firm specifics exist of two components. Each component exists of 4 firm specific which high loading factors (>0.4). This in in line with the expected division of firm specifics over the organizational identities of Banks and Fintechs shown in table 6.
After validating the two constructs, an independent T test is performed to test if the mean differences between the groups are statistically different. When looking at table 5, the mean values of the firm specifics can be compared for Fintechs and Banks. When comparing the expected values, in table 3, with the observed values, in table 5, six out of eight variables are in line with expectations and show significant relationships with the firm specific that belongs to the specific organizational identity as validated in the factor analysis. The independent T-test shows that there was a significant difference in the scores for the following firm specifics when the Fintech group is compared to the Bank group:
1) Safety_purpose Banks (M=73, 4, SD=19, 6) and Safety_purpose Fintechs (M=61.7, SD=21.1) conditions; t (170) =3.74, p = <0.001. These results suggest that consumers associate ‘having a safety purpose’ more with Banks than with Fintechs.
2) Role_society Banks (M=83.9, SD=16.1) and Role_society Fintechs (M=69.8, SD=23.0) conditions; t (129) =4.53, p = <0.001. These results suggest that consumers associate ‘having an important role in society’ more with Banks than with Fintechs.
3) Innovative_firm Banks (M=60.2, SD=22.5) and Innovative_firm Fintechs (M=73.5, SD=17.3) conditions; t (170) =-4.4, p = <0.001. These results suggest that consumers associate
31
‘firm is innovative’ more with Fintechs than with Banks.
4) Relationship_innovative Banks (M=56.3, SD=22.2) and Relationship_innovative Fintechs (M=71.5, SD=17.5) conditions; t (170) = -5.0, p = <0.001. These results suggest that consumers associate ‘the relationship with the consumer is based on innovative products and services’ more with Fintechs than with Banks.
5) Data_knowledge Banks (M=54.7, SD=21.7) and Data_knowledge Fintechs (M=67.5, SD=19.4) conditions; t (170) = -4.0, p = <0.001. These results suggest that consumers associate ‘knowledge on data and the internet’ more with Fintechs than with Banks.
6) Customer_centricity Banks (M=51.0, SD=24.8) and Customer_centricity Fintechs (M=63.6, SD=21.2) conditions; t (170) = -3.6, p = <0.001. These results suggest that consumers associate ‘consumer's needs and wishes are important’ more with Fintechs than with Banks. Summarized, for the organizational identity of Fintechs: the firm specifics: Innovative_firm, relationship_innovative, and customer_centricity, have a higher consumer association with Fintechs than with Banks. For the organizational identity of Banks, only the firm specifics safety_purpose and role_society have a higher consumer association with Banks than with Banks. The firm specifics trust and norms were expected to also have a significant higher value for Banks compared to Fintechs but this is not the case.
Overall, taking into account the factor analysis and the results from the sample T-test, the firm specifics do confirm that the organizational identities of Fintechs and Banks are significantly different and that innovative_firm, relationship_innovative, data_knowledge and customer_centricity are predictors of the organizational identity of Fintechs and that for Banks the firm specifics safety_purpose and role_society are predictors of the organizational identity of Banks.
32
Table 6 - Firms specifics expected to have a relationship with the Organizational_ID of Banks or Fintechs
Table 7 - Exploratory Factor analysis for the Organizational identity of Banks vs Fintechs Fintechs compared to Banks
1 Safety of money 2 Trust
3 Role in Society 4 Norms 5 Innovative
6 Relationship based on innovative offerings 7 Data knowledge
8 Consumer centricity
Significantly higher Organizational identity firm specifics
Significantly higher Significantly lower Significantly lower Significantly lower Significantly lower Significantly higher Significantly higher Banks Fintechs 1 Safety of money 0,15 0,60 2 Trust 0,24 0,65 3 Role in Society -0,12 0,70 4 Norms 0,00 0,58 5 Innovative 0,83 -0,02
6 Relationship based on innovative offerings 0,80 -0,05
7 Data knowledge 0,74 0,15
8 Consumer centricity 0,71 0,19
Extraction M ethod: Principal Component Analysis. Rotation M ethod: Varimax with Kaiser Normalization. Factors loadings over.40 appear in bold
33 Table 8 - Independent T-test for relationship of firm specifics with organizational _id
Acceptance of firm practice
Table 7 shows that on average the consumer acceptance level is 57.79 out of 100. The average acceptance level for the groups individually are for Banks M 67, 68 (SD 27.84) and for Fintechs group shows M 57, 92 (SD 24.2). There are three questions that are expected to measure the level of acceptance of the service by consumers (DV).A scale reliability test is performed and the three questions give a cronbach’s alpha of 0.954 which is a score that indicates high reliability. The cronbach’s alpha would not increase if one of the three items would be deleted. A factor analysis test was performed and meets the condition of the required KMO test (>.6). One component has been identified above the Eigenvalue of 1, meaning that the set of three questions all measure one factor.
Perceived fit between firm practice and organizational Identity
Several observations can be made when taking into account the expected outcome versus the observed outcome of the factor analysis. A number of questions should measure two constructs: 1) Acceptance_FP, and 2) Fit_FP. The questions on attractiveness, expected benefit and intention to use should form the construct Acceptance_FP. The questions on idea, right company and perception should form the construct Fit_IDFP. The outcome of the factor analysis (table 9) shows indeed that items: attractiveness, benefit, intention to use show a high
Mean SD Mean SD t df Sig.
(2-tailed) Mean Difference Std. Error Difference 1 Safety_ purpose 73,3958 19,59349 61,7632 21,06679 3,74 170 ,000* 11,63268 3,11021 2 Trust 66,8229 26,71447 68,8421 21,94299 -0,544 169,753 0,587 -2,01919 3,71072 3 Role_Society 83,8750 16,05271 69,8289 22,96745 4,527 128,996 ,000* 14,04605 3,10244 4 Norms 75,0313 21,63450 73,6579 20,64723 0,424 164,14 0,672 1,37336 3,23803 5 Innovative firm 60,1563 22,53714 73,5395 17,32470 -4,403 169,869 ,000* -13,3832 3,03976 6 Relationship_ 56,2708 22,16563 71,4474 17,53731 -5,013 170 ,000* -15,1765 3,02732 7 Data_knowledge 54,6563 21,66629 67,5000 19,36974 -4,044 170 ,000* -12,8438 3,17591 8 Consumer_centricity 50,9583 24,75008 63,6316 21,19927 -3,55 170 ,000* -12,6733 3,56988
*mean difference is significant at the 0.05 level (2-tailed). Variable
Firm specifics
Organizational Identity
34
communality (+ 0.8). The three items that were expected to measure the construct Fit_IDFP do not show high communality amongst each other. But what is noticeable is that the item idea shows a similar communality as the items that should measure acceptance. The item right company is also relatively high in communality (0.7). The item perception on the other hand shows a relatively low number compared to the other items (0.5). Therefore it looks like the items ‘idea’ and ‘right company’ are more likely to measure the same construct as the items belonging to the variable Acceptance_FP. For the item ‘perception’ where the respondents were asked if they think that there is a fit between the service offered and the firm, which is a very direct question, the analysis shows that there is less communality with the other questions.
Theoretically, fit and acceptance are two different constructs as consumers that accept a firm practice can still value the perceived fit between the organizational identity and the firm practice differently. Therefore the factor analysis is perfumed again, but with forcing the extraction of two components (table 10). The second performed factor analysis, shows that item “perception” compared to all other items is the only item that shows a contrasting high loading value in the rotated Varimax analysis. The item perception explains 10.6%, while the first component which measures Acceptance_FP explains a variance of 58.7%. Therefore the item perception will be treated as a separate construct, eliminating items idea and right company from the measurement scale. This means that the construct fit will be measured by one question only.
Table 9 - Exploratory factor analysis for Acceptance_FP and Fit_IDFP
Questionnaire items Acceptance_FP (1-3) and Fit_IDFP (4-6) Extraction
1 Level of attractiveness 0,86
2 Level of expected benefit 0,86
3 Level of intention to use 0,86
4 Idea to provide this service 0,87
5 Right company to provide this service 0,69
6 Fit of firm practice with organizational identity 0,51
35 Table 10 - Factor analysis for the fixed constructs Acceptance_FP and Fit_IDFP
Correlation analysis
Examination of the correlation matric (table 11) shows that none of the control variables are significantly correlated with Acceptance_FP or Organizational_ID. The control variable Banking_sector (r=0.164, N=47, P<0.05) is significantly positively correlated with Fit_IDFP. When examining the main variables the correlation analysis shows that Fit_IDFP correlates with both Acceptance_FP (r=0.585, N=172, P<0.01) and Organizational_ID (r=0.304, N=172, P<0.01). As the correlation of the variables is high, a collinearity analysis is performed giving VIF results well below 5 for both variables, therefore complying with the collinearity assumption.
Table 11 - Correlations among key study variables
Questionnaire items Acceptance_FP (1-3) and Fit_IDFP (4-6) Acceptance_FP Fit_IDFP
1 Level of attractiveness 0,90 0,32
2 Level of expected benefit 0,87 0,36
3 Level of intention to use 0,90 0,32
4 Idea to provide this service 0,88 0,36
5 Right company to provide this service 0,56 0,68
6 Fit of firm practice with organizational identity 0,26 0,93
Extraction M ethod: Principal Component Analysis. Rotation M ethod: Varimax with Kaiser Normalization. Factors loadings over.40 appear in bold
1 2 3 4 5 6 7 8 9 10 11 12 13 1 Acceptance_FP -2 Organizational_ID ,004 -3 Gender ,109 -,052 -4 Age -,128 -,060 -,173* -5 Government_sector ,058 ,081 ,165* ,122 -6 Banking_sector 0,14 0,14 0,00 0,09 -,247** -7 Fintech_sector -,025 -,084 -,137 -,094 -,070 -,106 -8 High_School ,064 -,060 -,042 -,028 -,062 -,008 -,027 -9 MBO -0,01 -0,04 -0,03 0,01 0,04 -0,09 0,03 -0,06 -10 HBO ,049 -,012 -,085 -,100 -,056 -,129 ,070 -,126 -,322** -11 Bachelor -,073 ,078 -,035 -,023 ,082 -,082 ,054 -,053 -,134 -,280** -12 Master -0,02 0,01 0,15 0,12 0,00 ,253** -0,12 -0,11 -,280** -,584** -,244** -13 Fit_IDFP ,585** ,304** -,051 -,006 -,008 ,164* ,020 ,007 -,053 ,071 -,077 ,012
-**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
36
Testing the model
A hierarchical multiple aggression was performed to investigate the ability of Organizational_ID to predict a higher level of Acceptance_FP and the ability of Fit_IDFP to positively moderate the relationship between Organizational_ID and Acceptance_FP, after controlling for gender, age, education level, the industry of the consumers’ employment. The regression coefficients are listed in table 12.
Step 1 of the regression model tests the impact of the control variables on the model: Acceptance_FP=β0(constant)+β1*gender+β2*age+β3*government_sector+β4*
banking_sector+β5*Fintech_sector+ β6*High_school+ β7*MBO+ β8*Bachelor+ β9*Master. Step 2 adds the independent variable β10*Organizational_id to the model.
Step 3 of the regression adds the independent variable β11*Fit_IDFP to the model. Step 4 of the regression test for the interaction of the independent variables Organizational_id and Fit_IDFP and therefore the variable: β11* Organizational_id*Fit_IDFP is added to the model.
In the first step, the effect of the control variables on Acceptance_FP is tested. The model shows not to be significant F (9,162) = 1,43, p=0,179 which is >0.05. The control variable Banking_sector shows to have a significant relationship with acceptance (β= 0.20, p <0.05). This suggest that consumers working in the Banking sector have a higher level of acceptance for the firm practice when offered by Fintechs compared to Banks.
Step 2 adds Organizational_id to the model and also shows not be significant F (1,161) = 0.13, p= 0,714 which is > 0.05. This outcome rejects H1. The variable Organizational_id records a Beta value of (β=- 0.03, p >0.05). The control variable Banking_sector still shows to have a significant relationship with acceptance (β= 0.20, p <0.05).
After adding Fit_IDFP in step 3 to the model as a predictor, the model shows F (1, 160) = 99.42, P<0.001 and therefore statistically significant. This explains 39% of the variance of
