Drivers of Mobile Banking Behavior and the use of M-banking applications : an empirical study based on Technology Acceptance Model (TAM)

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Drivers of Mobile Banking Behavior and the Use

of M-banking Applications

- An Empirical Study based on Technology Acceptance Model (TAM)

Program: MSc Business Administration – Marketing track Supervisor: Prof. Umut Konus

Student: Meixin Zeng Student number: 10086188 Final version: 24 November 2016

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University of Amsterdam, Meixin Zeng 10086188 Page 2

Statement of Originality

This document is written by Student Meixin Zeng 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.

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Abstract

Purpose

The purpose of this research is to explore the drivers and factors influencing the mobile banking behaviors. This paper seeks to evaluate the relationships between the modified technology acceptance model (TAM) and customers’ characteristics based on demography, psychographics traits from customer segmentation and usage intensity. It aims at showing the impacts of personal characteristics on the technology adoption in the context of mobile banking and verifying the substitution effect of new technology (switching behavior).

Design/methodology/approach

This is an empirical study based on an online distributed survey containing 34 questions measured by a 7-point Likert scale. In total, 222 surveys were valid according to our defined criterion, for an effective recovery rate of 41.5%. Factor analysis and regression analysis were used to examine the hypotheses.

Findings

The results show first that perceived usefulness has positive effect on intention to switch. Age has negative impact on both intention to switch and actual switch behavior. Male is proved to be more likely to have intention to switch from other banking channels to m-banking application than female. Moreover, motivation to conform innovativeness and price consciousness have positive influences on intention to switch. Finally, usage intensity of mobile banking app is not only a significant predictor for actual switch but also a positive moderator for the relationship between intention to switch and actual switch.

Research implications

This study considers the substitution effect of new technology in the technology adoption theory by testing switching intention and actual switch in the context of banking. Psychographics traits are suggested to be further exanimated for its impacts on attitude in the technology acceptance model to explain the mobile banking behavior.

Practical implications

It is recommended to consider the psychographics traits when conducting a mobile strategy for banking. Increasing the situations of using the mobile banking apps will evoke customers’ actual migration to mobile banking apps. Online banking users are the most important target group in mobile banking migration.

Originality/value

This paper applies the psychographics traits as a concept of users’ characteristics that have not been discussed in technology adoption literature. Secondly, it adds gender, age and education as control variables in all the tests where different result than antecedent on PEOU was found. Thirdly, it is highly original to find significant substitution effect of mobile banking apps on online banking. Therefore, it adds more value to discuss intention to switch and switch behavior in this study.

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List of Tables

Table 1. M-banking users ... 7

Table 2. Focus of mobile marketing research ... 13

Table 3. Prior research overview ... 23

Table 4. Research Hypotheses summary ... 33

Table 5. Table of variables ... 36

Table 6. Demographic characteristics of the respondents. ... 37

Table 7. Summary of measurement scales ... 39

Table 8. Correlation matrix for hypothesized variables ... 41

Table 9. Regression results of Intention to Switch (PU/PEOU) ... 43

Table 10. Regression results of Actual Switch (PU/PEOU)... 44

Table 11. Moderating effect of psychographics variables ... 47

Table 12. Regression result of Intention to Switch (psychographics) ... 48

Table 13. Moderating effect of Usage Intensity ... 49

Table 14. Hypotheses results ... 50

Table 15. Correlation matrix for non-hypothesized variables ... 52

Table 16. Regression result of actual switch (Usage Intensity) ... 53

List of Figures

Figure 1. Examples of Mobile B2C business models ... 13

Figure 2. The percentage of mobile and internet transactions 2010-2017 in China ... 14

Figure 3. M-banking application, channel and service ... 15

Figure 4. TAM fundamental elements ... 20

Figure 5. Research objectives ... 24

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1. Introduction

Driven to generate value-in-use, many organizations have become multi-channel service providers by increasing the number of service channels available to their customers in an effort to improve the efficiency, cost-effectiveness, and consistency of their frontline operations (Fitzsimmons & Fitzsimmons, 2004). Multichannel is defined by Rangaswamy & Van Bruggen (2005) as “simultaneously offering their customers and prospects information, products, services, and support (or any combination of these) through two or more synchronized channels”. Research from Kumar & RajkumarVenkatesan (2005) also showed that multichannel shoppers create better benefits than single-channel shoppers for organization, because they are aware of options available to them and choose the mediums most accessible for them.

A general review of literature reveals that many recent multichannel management studies have been putting a lot of attentions on the new channels based on mobile technology. The increasing number of users and the benefits of mobile banking reported in numerous social media also bring my attention to this specific new channel in multichannel management literature. This paper is specifically focused on a mobile instrument – mobile applications in the context of banking industry, providing customers with specific services to fulfill banking purposes (such as transferring money, checking balance or account information, paying bills, making deposit to account, checking interest rate or managing your accounts...) via smartphones.

Several news and presses (Gahran, 2011; CFCA, 2014; Whilhem, 2016) reveals the emerging and booming of the use of mobile banking, especially in the form of mobile apps. As shown in Table 1, over 59 million people, which accounts for 8.6% of global population, in are classified as mobile banking users, according to the statistic in 2011 from International Telecommunication Union. Banks also recognize that mobile banking apps are crucial to their future, because it is more cost-efficient by requiring fewer employees to handle transactions. Processing a check through a teller costs a bank on average $4.25, compared to 10 cents via a mobile app, according to Javelin Strategy and Research (2013).

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University of Amsterdam, Meixin Zeng 10086188 Page 7 Table 1. M-banking users

As the facts above mentioned, technology development facilitates more cost efficiency and time saving for both company and customer for banking. Lots of banks already employed the new technology to serve their customers with more efficient and convenient solutions; meanwhile in order to utilize the new technology, they are eager to understand how their consumers or users adopt the new technology provided? And what are the drivers behind their adoption behavior?

Secondly, the numbers of usage growth not only indicate the increase in the usage of mobile apps but also show the decrease in the usage of other channels on historical level, which implies a switching behavior. This switching behavior can be perceived as a process of adopting a new technology in banking context according to Lee, Tsai and Lanting (2011). Therefore, this study will mainly illustrate this migration phenomenon instead of normal adoption discussed in most of the literature. The main research question is formed: “what are the drivers or predictors for banking customers to switch from other conventional banking channels to mobile banking applications?”

In order to find out the answer for the research question proposed above, several researches about the drivers for technology adoption are reviewed. Eventually, the TAM model is employed following the advices of Lee, Tsai and Lanting (2011), who explored the factors that affect the attitude and intention to switch in the context of online banking. Regarding the factors that influence users’ intention to use and adoption in TAM, the features of the new technology itself, such as PU, PEOU and characteristics of the users were the major clusters to develop the extended TAM according to Bhattacherjee (2001) and Lee, Tsai, Lanting (2011). There is no existing research linking the technology adoption theory with customer segmentation perspective. Our extended TAM model is proposed with a number of psychographic traits added and adapted from the study of customer segmentation developed by Konus et al. (2008).

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University of Amsterdam, Meixin Zeng 10086188 Page 8 Here is the explanation about how our study is processed: We are firstly going to overview the existing literatures with regards to multichannel marketing, studies related to mobile apps in banking sector, TAM, psychographics traits and the effects of general mobile experiences on the intention to switch to mobile banking apps. Then hypotheses will be illustrated and presented in an integrated conceptual framework. A web-based survey will be spread out and data collected for further analysis. Regression analysis will be sequentially operated after measurements are clarified. The test results will be presented with indication to the findings of this paper. In the end of this study, we will be conducting research conclusions and providing future research suggestions considering current limitations. As a result, this study will contribute to the customer-centric view on multichannel (Schoenbachler & Gordon, 2002) by adding switching intention of customers within the multichannel context, and linking the viewpoint of customer segmentation to technology acceptance theory.

This whole research and results will help managers and researchers engage their existing users from conventional channels to new channels and provide knowledge on how to bring their customers to the company’s new stage. Consequently conversion will be more effective and operational costs will be reduced. The empirical study helps managers better understand the psychographic associated TAM that triggers the switching behaviors. They can understand the switching behavior better by the empowered TAM with the knowledge of their customer segmentations. Therefore, this study also provides management implications for the managers who would like to understand how to reach different customers in order to develop a better strategy to improve their mobile banking service.

Literature Review

This chapter summarizes existing studies of m-banking adoption and maps the major theories. First of all, it provides a general understanding of multichannel marketing (section 2.1.) incorporating the role of mobile (section 2.1.1). Secondly, it explains the appearance of mobile application within the financial industry (section 2.2.) and illustrates the drivers for the adoption of m-banking (section 2.2.1.). And lastly, the research gap is identified by reviewing the existing literatures of TAM, psychographics traits and other related factors (section 2.3.). In the end main research question will be developed to define the direction of the study.

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University of Amsterdam, Meixin Zeng 10086188 Page 9 2.1. Multichannel Marketing

In this sub-chapter, there is a summary of definitions of channels, multichannel and multichannel marketing, key issues and research areas are listed out and the motivation for further research on multichannel marketing are explained.

Channel is defined as “a customer contact point or a medium through which the firm and the customer interact” (Neslin et al, 2006). In general, a channel might be a retail store, a web site, a mail order catalogue, or direct personal communications by letter, email or text message (Weinberg, Parise & Guinan, 2007). In banking sector, channels are defined as the different channels for customers to access their banking services, which includes Automated Teller Machines, branch in a retail location, call center, mails, SMS banking, Internet banking, relationship managers (mostly for private banking or business banking, often visiting customers at their homes or businesses), phone banking (mobile Internet browser and banking apps), Direct Selling Agent etc (Calisir and Gumussoy, 2008).

The diverse combinations and integration of different channels contribute to the concept of multichannel. Thus, multichannel studies refer to the research on the comprehensive practices to interact with potential customers on different platforms, such as via text messaging, on a website, email and GPS to track the location of a customer and their proximity to the product or service (Jared, 2014). Multichannel is defined by Rangaswamy and Van Bruggen (2005) as “simultaneously offering their customers and prospects information, products, services, and support (or any combination of these) through two or more synchronized channels” And the marketing strategies to reach the customers who use more than one channel to interact with firms is defined as “multichannel marketing” (Rangaswamy and Van Bruggen, 2005). In the most simplistic term, multichannel marketing is all about choice (Schoenbachler and Gordon, 2002). The objective of the companies doing multichannel strategy is to make it easier for consumers to consume in the most appropriate or beneficial way.

One of the key research areas on multichannel marketing is multichannel customer management (MCM), which is defined as “the design, deployment, and evaluation of channels to enhance customer value through effective customer acquisition, retention, and development” (Neslin, Grewal, Leghorn, et al. 2006). Some key issues and future directions

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University of Amsterdam, Meixin Zeng 10086188 Page 10 for Multichannel Customer Management studies are summarized by Neslin & Shankar (2009), pointing out the important insights on issues like channel choice (Kumar and Venkatesan 2005; Kushwaha and Shankar 2008), channel migration (Ansari, Mela, and Neslin 2008), allocation of marketing efforts (Kushwaha and Shankar 2008), and the value of multichannel versus single channel customers (Ansari, Mela, and Neslin 2008; Kushwaha & Shankar 2008).

Besides the cost-efficient advantages of developing multichannel for different customers’ segments, multichannel research also triggers better management of results and sales, therefore delivers more value to the companies by using many communicative platforms to reach their audience, which increases the chances of receiving feedback from a variety of customers. As Stone et al (2002) mentioned in his empirical study, multichannel development enables the sharing of cross-channel customer data so as to build a complete sales data base which will then help to maximize opportunities and discover the risks of brand damages (Stone et al, 2002). Kumar and Venkatesan (2005) suggest that multichannel shoppers create better benefits than single-channel shoppers, so that customers who are already shopping through all the available channels should be targeted when the organizations added new transaction channels. In the same study, multichannel shoppers are more loyal (as measured by share of wallet and likelihood of being active) and more profitable (as measured by past customer value) than single-channel shoppers have been proven, because they are aware of options available to them and intend to purchase products in the mediums most accessible to them (Kumar & Venkatesan, 2005).

2.1.1. Mobile Channels in Multichannel Marketing and M-commerce

Mobile channel is gaining more attention within the multichannel marketing management; however, the literature on mobile marketing is somewhat inconsistent and highly fragmented (Leppäniemi, Sinisalo, & Karjaluoto, 2006). In this sub-chapter, the difference between conventional channels and mobile channels is explained; the related definitions of mobile channels are summarized. Furthermore, an overview of current mobile channels studies is provided.

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University of Amsterdam, Meixin Zeng 10086188 Page 11 Conventional marking channel is presented by “brick and mortar” stores, shopping centers and malls, which are operating in the land based world of physical facilities and real people (Rosenbloom, 2012). While the traditional marketing channels continue to exist, and thrive, the development of new technologies keep challenging this physical land-based way to shop and sell. Even Wal-Mart, the giant of traditional physical channel, had developed its online supermarket to exploit the latest internet development. The online channel shares several advantages over the offline counterparts: Greater convenience and accessibility, better information control and interactive tools which facilitate the customer decision making (Rosenbloom, 2012). The introduction of cell phone, especially smart phone and the wide spreading of wire-free internet boost the involvement of mobile in the online channel.

A general review of literature reveals that many definitions on marketing through the mobile channel have been proposed by marketing practitioners and scholars. Since research in mobile channels is relatively recent and the technology of mobile usage develops rapidly in nature, some of these conceptualizations are similar; however, there is a lack of consistency as to the definition of the marketing through mobile channel. Some studies describe mobile as an extension of online channel (Brassington and Pettitt, 2003), some studies argue that mobile channel should be considered separately from online/wireless studies due to the mobility of its underlying network (Leppäniemi, Sinisalo, & Karjaluoto, 2006; Shankar et al. 2010). Leppäniemi, Sinisalo, & Karjaluoto (2006) conduct a comprehensive overview revealing that collectively, the definitions represent four major approaches to marketing through the mobile channel: (1) mobile marketing (e.g. Kalakota and Robinson 2002, Scharl et al. 2005, Bauer et al. 2005); (2) mobile advertising; (3) wireless marketing (Brassington and Pettitt 2003); and/or (4) wireless advertising. The aspect of ‘wireless vs. mobile’ needs clarification because wireless is not necessarily mobile (Balasubramanian et al. 2002; Leppäniemi, Sinisalo, & Karjaluoto, 2006). A wireless access has very limited mobility within the range of the access point. However, true mobility can be reached by the mobile network across the whole area covered. Based on this distinction, mobile as a concept provides the best conceptual foundation for the marketing through mobile channels (Leppäniemi, Sinisalo, & Karjaluoto, 2006). Thus, mobile channel should not be perceived as an extension of online channel due to the distinction mentioned above, studies on online channel and mobile channel should be persuaded differently.

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University of Amsterdam, Meixin Zeng 10086188 Page 12 This paper is specifically focused on a mobile instrument – mobile applications providing specific services via smartphones to customers. Therefore, we follow the stream of mobile marketing and further investigate the mobile applications as a marketing tool that allow firms to provide services to customers in the purpose of enhancing profitability and customers’ relationship.

Leppäniemi, Sinisalo, & Karjaluoto (2006) define Mobile marketing as the “use of the mobile medium as a means of marketing communications”. This definition emphasizes the essential role of communication in establishing and maintaining profitable customer relationships. The follow-up study introduced by Shankar and Balasubramanian (2009) presents their view of mobile marketing as the “two-way or multi-way communication and promotion of an offer between a firm and its customers using a mobile medium, device, or technology”. This definition become most commonly cited and widely accepted and it clearly indicates two separate roles of communication and promotion. Scharl et al., (2005) define mobile marketing as using a wireless medium to provide consumers with time- and location-sensitive, personalized information that promotes products, services and ideas, thereby benefiting all stakeholders. All in common, mobile marketing refers to marketing activities and programs performed via mobile phone in mobile commerce.

Although now the definition of mobile marketing is clear, there is still no commonly accepted classification for mobile marketing studies due to the fragmentation of existing literature. However, conceptual consistency is essential to promote a shared understanding; therefore we follow the classification of mobile marketing studies suggested by Leppäniemi, Sinisalo, & Karjaluoto (2006). According to them, there are 4 main focus of mobile marketing research: consumer, business and management, and general (see Table 2). This paper is mainly studying the drivers and motivations that trigger consumers’ behavioral intention and or actual behavior toward switching from current banking channels to mobile banking applications, which intent to contribute to the consumer-centric studies. Meanwhile, our ambitious is to help business and firms understand their consumers better by optimizing their business models. Thus, this study also overlaps the focus of business and management.

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University of Amsterdam, Meixin Zeng 10086188 Page 13 Table 2. Focus of mobile marketing research

Source: Leppäniemi, Sinisalo, & Karjaluoto (2006)

As mobile is the tool or medium to execute marketing strategy (Leppäniemi, Sinisalo, & Karjaluoto, 2006), the content of the mobile business attached to the mobile marketing should be further clarified. The contents provided by current mobile channel have been summarized by Leem et al., (2004). The mobile business is subdivided into commerce, intermediary and information models. Mobile commerce has 3 difference forms; our study about banking is categorized into the “service” stream (as shown below in Figure 1).

Figure 1. Examples of Mobile B2C business models

Source: Leem et al., (2004)

The widely dissemination of mobile phone has created the mobile commerce (Leppäniemi, Sinisalo, & Karjaluoto, 2006). Mobile commerce (m-commerce) refers to any transactions, either direct or indirect, with a monetary value, implemented via a wireless

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University of Amsterdam, Meixin Zeng 10086188 Page 14 telecommunication network (Ko et al, 2009). It has a strong impact on industries like e-commerce in general and transformed mobile e-commerce into a major driving force for the next wave of e-commerce (Leppäniemi, Sinisalo, & Karjaluoto, 2006). The growth and use of mobile commerce as an emerging technology has the potential to dramatically change the way consumers make business nowadays. In the literatures reviewed in this study, there are only small differences in the existing mobile commerce definitions. According to Clarke (2001), any monetary transactions conducted via mobile terminals may be defined as mobile commerce. In the later study of Leppäniemi, Sinisalo, & Karjaluoto (2006), mobile commerce is defined as a new type of e-commerce transaction conducted through mobile devices using wireless telecommunication networks and other wired e-commerce technologies. Similar descriptions are found in Tiwari, Buse & Herstatt (2006) that it is the electronic commerce transactions carried out via mobile phones and wireless terminals. To sum up, it is appropriate to define mobile commerce as a business model that enables the consumers to complete all steps of a commercial transaction by a mobile device.

Figure 2. The percentage of mobile and internet transactions 2010-2017 in China

In mobile commerce, the popularity and glamour of mobile marketing is in a rise. The Figure 2 shows the percentage of mobile and internet commerce transactions from 2010 to 2017(estimate) in China, the transactions by mobile terminals increase dramatically by nearly 10% ever since 2013, largely occupying the shares used to be the wired channels. Therefore, mobile commerce has been a spotlight over the last few years and its trend in reshaping

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University of Amsterdam, Meixin Zeng 10086188 Page 15 marketing (by introducing new business models as well as offering some advantages to customers, retailers and GSM operators) never fades.

2.2. Mobile in Banking and M-Banking Applications

As explained in last chapter that banking was one of the service contents in mobile commerce setting (Leem et al., 2004), this sector we deep dive in the development of mobile banking. Literatures show that various terms are used to define mobile banking, including m-banking (Barnes & Corbitt, 2003), m-payments, m-transfers, m-finance (Donner and Tellez, 2008). Cruz et al. (2010) point out the difference between m-banking and m-payments and argue that, “if a bank is not directly involved in the instrumental gratification of a service offered, it is usually called a mobile payment (m-payment)”. Pousttchi & Wiedemann (2006) also differentiate mobile-banking from other terms as “A product or service offered by a bank or a microfinance institute (bank-led model) or MNO (non-bank-led model) for conducting financial and non-financial transactions using a mobile device, namely a mobile phone, smart phone, or tablet”, which has been widely accepted by recent researchers. From the m-banking service perspective, Pousttchi & Wiedemann (2006) demonstrate an ecosystem comprising several applications, channels, and methods for m-banking, as well as major services offered through m-banking channels depicted in Figure 3 below:

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University of Amsterdam, Meixin Zeng 10086188 Page 16 Source: Pousttchi & Wiedemann (2006)

Mobile banking (m-banking) is one of the latest mobile innovations with its diffusion in banking industry. Although automated teller machine (ATM), telephone, and Internet banking have already delivered traditional banking services, but as an innovative service rendering channel, m-banking is likely to revolutionize the market (Safeena, at al., 2012). The prevailing of smart phones directly urges the banking services integrated with mobiles, prompting banks, microfinance institutions, software companies to fulfill such needs, extend their client reach (including to unbanked populations), improve customer retention, enhance operational efficiency, increase market share, and provide new employment opportunities (Shaikh & Karjaluoto, 2015).

In the sector of mobile banking which involves mobile phone, smartphone, or tablet, the usage of mobile banking apps shows the extraordinary growth. A news reports by Whilhem (2016) mentioned that the mobile banking is of great popularity among the population. “55% of Americans access their accounts through mobile banking options (including laptops, tablets and smart phones) two to three times per week; 26% said they bank using mobile devices four or more times per week” (Whilhem, 2016). Gahran (2011) reports that online banking payment (computer and mobile)in the fourth quarter of 2010, almost 30 million Americans accessed their bank, credit card or brokerage accounts from a cell phone or tablet, up 54% from the same quarter a year earlier. This number is more significant than the growth in computer access. Especially, three primary mobile banking channels show below (note that some people use more than one channel):

Smartphone/tablet app: 10.8 million users, 120% increased in 2009-2010 Mobile web browser: 18.6 million users, 58% increased in 2009-2010 SMS text messaging: 8.1 million users, 35% increased in 2009-2010

Among all the numbers increased in mobile payment, the growth in using mobile banking application (120%) caught the most of our attention, which is a unique experience requiring a smart phone. For traditional banks, mobile application was initially developed as a variant of their online service in virtual market (Rayport and Sviokla, 1994) in order to offer their

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University of Amsterdam, Meixin Zeng 10086188 Page 17 customers a completed service covering all locations. Banks also recognize that mobile banking apps are crucial to their future, because it is more cost-efficient by requiring fewer employees to handle transactions. Processing a check through a teller costs a bank on average $4.25, compared to 10 cents via a mobile app, according to Javelin (2013). In the aspect of users, the banking apps are widely labeled as “convenient and time-efficient” (as over 70% users commented), and “job-needed” (as nearly 40% users commented). Concerning the functions, the top 5 frequently used functions are: account overview, bank transfer, bank payment, bill payment, and credit card transactions. Moreover, a literature review by Strom et al (2014) shows that “Mobile Internet users perceived mobile devices as enjoyable and timely, recognizing their three primary benefits: convenience (flexibility in terms of time and location), companionship and efficiency compared to the PC” (Strom et al, 2014).

In addition, approximately half of all mobile subscribers remain unbanked, with limited access to traditional financial services (Shaikh & Karjaluoto, 2015). These data all suggest a huge room for mobile bank developments in the near future. These figures also evoke further investigations of any persistent adoption issues in m-banking.

2.3. Drivers of M-Banking

In order to find out the answer for the research question proposed, several researches about the drivers for technology adoption in the contaxt of online/mobile banking are reviewed in this empirical study.

One of the center points of mobile marketing is customer acceptance. Among the various drivers of customer acceptance, the entertainment value and information value seem more visible, as the cell phones serve as the innovation for advertisement (Bauer et al. 2005). Those driving factors of mobile sales channel have also been included in Leppäniemi and Karjaluoto (2005) with a framework to assess the customers’ attitudes in accepting those mobile advertisements. Meanwhile, the factors that decide the customer acceptance of mobile marketing are tested in Barnes and Scornavacca (2004). They distinguished 3 key variables that exert the most influence on consumers’ acceptance of mobile marketing from a number of empirical experiments, which are user’s permission, wireless service provider control and brand trust. Furthermore, in Carroll et al. (2007), an empirically model of mobile marketing

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University of Amsterdam, Meixin Zeng 10086188 Page 18 consumer acceptance were tested and devised based on Barnes and Scornavacca (2004). Four factors are concluded to significantly impact the acceptance of mobile marketing: permission, content, wireless service provider control and the delivery of the messages. Basing on their analysis, these studies express their optimism of deploying mobile channel in marketing, with special focuses on those abovementioned variables.

On the later stage, most of the studies have specified the implementations of a certain technology incorporating with technology adoption studies. Some studies try to analyze the features of technologies to seek the insights such as Innovation Diffusion Theory (IDT) developed by Roger (2005). Some identify the influential factors embedded in the adoption activities, such as the Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. (2003) and the Technology Acceptance Model (TAM) contributed by Davis (1989). The TAM model has been widely employed in the mobile banking studies with an intention-driven psychology (Bankole et al., 2011). This model suggests that the perceived usefulness and perceived ease of use to be the basic factors that determines the technical implementation and utilization (Shaikh & Karjaluoto, 2015). To add value to the persuasiveness of TAM in explanation of Mobile channel adoptions, several economic and demographic factors and external variables, such as personal inclination to innovations (Chitungo and Munongo, 2013), perceived risk, perceived cost of use, compatibility with lifestyle and needs (Hanafizadeh et al., 2014) are built to the extension of TAM model.

The Innovation diffusion theory (IDT) is the second widely adopted theories in technological adoption decision makings. This theory has its focuses on the features of technologies: relative advantage, compatibility, complexity, observability, and trialability (Roger, 2005). However, the perceptions and attitudes of customers are ignored (Bhattacherjee, 2000). The third commonly used theoretical framework is the unified theory of acceptance and use of technology (UTAUT) developed by Venkatesh et al. (2003). The UTAUT aims to explain users’ intentions to use an information system and subsequent usage behavior (Venkatesh et al., 2003). Beside the variables included in the TAM, perceived usefulness as well as other behavior factors, it extends the TAM model into a model of 4 determinants: performance

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University of Amsterdam, Meixin Zeng 10086188 Page 19 expectancy, effort expectancy, social influence, and facilitating conditions (Shaikh & Karjaluoto, 2015), however, the cultural influences were elided (Im et al., 2011).

The abovementioned technology acceptance theories are mainly employed to study the drivers of mobile banking adoption. They have their own advantages but also drawbacks exist. Therefore, to adopt the most relevant and suitable model, this study follow the research direction of antecedent study from Lee, Tsai & Lanting (2011), thus TAM is most applicable and proper model to investigate the switching behavior in this study. The next sub-chapter, we will further explain how TAM applied in existing researches.

2.3.1. Technology Acceptance Model

The technology acceptance model (TAM) is utilized to investigate the intention and behavior towards switching to mobile banking using the theoretical model from previous empirical study conducted by Lee, Tsai & Lanting (2011). According to TAM, it suggests that when a new technology is faced with potential users, a number of factors influence their decision about how and when they will use it, notably:

Perceived usefulness (PU): defined by Davis (1989) as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis 1989).

Perceived ease-of-use (PEOU) defined this as “the degree to which a person believes that using a particular system would be free from effort” (Davis 1989).

In TAM, the external variable (new technology in the case) changes people’s Perceived usefulness (PU) and Perceived ease-of-use (PEOU), both of them then influence the user attitude towards using, then the attitude turns into intention, which triggers the final use decision. Meanwhile, Perceived ease-of-use (PEOU) may also have an impact on Perceived usefulness (PU), which will then influence the intention directly. The association of TAM can is shown in Figure 4.

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University of Amsterdam, Meixin Zeng 10086188 Page 20 Figure 4. TAM fundamental elements

Source: Davis (1989)

TAM has been continuously and vigorously extended for use with some models, such as trust, perceived risk, computer self-efficacy, and gender (Lee, Tsai & Lanting, 2011; Lai and Li, 2005). To fully analyze behaviors towards use, the factors that are significant in using and accepting technology have been scrutinized across wide-ranging user technology and application. Therefore, it is rational to say that the foundation for user technology acceptance research has been deeply entrenched.

As previously mentioned, precedent researches in adoption of new banking channels have mostly focused on the features of the new technology itself, such as its perceived usefulness (PU) and perceived ease of use (PEOU) adopted from the technology acceptance model (TAM) and characteristics of the users (personal creativity, professional background and online experience).

Although TAM has been extensively verified with factors that affect users’ intentions toward new banking platforms, prior research has mostly focused on the concept of attitude towards using and behavioral intention to use (Davis et al. 1989; Agarwal & Prasad 1999; Porter & Donthu 2006). However, researchers yet failed to address the antecedents of the specific switching behavior, until Lee, Tsai & Lanting (2011) proposed “attitude and behavioral intention towards switching”. The logic behind the terms of switching is because before a consumer decides to accept this new technology, the behavioral intention to switch must take precedence. When consumers make a choice between several platforms, the viewpoints of

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University of Amsterdam, Meixin Zeng 10086188 Page 21 ‘‘intention towards switching” (Lee, Tsai & Lanting (2011) take place of ‘‘attitude towards use” and ‘‘intention to use” in reflecting the substitution effect in terms of banking services. Therefore, while consumers make a choice among several banking channels, the traditional marketing view of ‘‘attitude towards use’’ and ‘‘intention to use’’ (Lin 2006, Porter and Donthu 2006) are not suitable to describe the switching behaviors and substitution effect of new technology in this study. In this case, ‘‘intention towards switching’’ proposed by Lee, Tsai & Lanting (2011) in reflecting the substitution effect is used to set up the overall research and further investigation.

2.3.2. Psychographic traits

Several psychological factors are also included in the TAM model. The most commonly chosen ones are: time pressure, the Motivation to conform Innovativeness, and price consciousness. The time pressure represents the time restrictions in purchasing within a certain sales channel. It is defined as the extent to which the customers feel pressured to make decisions quickly (Kleijnen et al, 2007) The switching intention and actions primarily take place from the discontent with the low time efficiency of the current procurement channels as said by several previous studies (Scheer et al 2010). Another trait that is frequently mentioned in the TAM relations is the motivation to innovativeness. There are certain proportions of individuals who are keen on the products tagged with cutting-edge technologies and scientific advancements thus examples of the incorporation of innovative elements can always be found in advertisements. Innovativeness refers to the degree which a person prefers to try new and different products and seek out new experiences (Midgley and Dowling, 1978). The last but not the least, we adopt the price consciousness from Konus (2008) and Lee, Tsai & Lanting (2011) as one of our independent variables. Price consciousness is defined as “the degree to which consumers focus on paying low prices (Lichtenstein et al 1990), Tanford et al (2010) disclose in his empirical studies that the price sensitivity can be a decisive drive for the patrons to change hotels and thus their loyalty (Tanford et al, 2010), as a price-conscious consumer seeks to minimize the price paid for an item, which relates to savings (Konus et al, 2008).

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3. Gap and Research Question

The purpose of this research is to explore the drivers and factors influencing the mobile banking behaviors. Our main objective is to explore how the identified factors affect the migration process from traditional banking channels to mobile banking apps. We attempt to evaluate the relationships between the modified technology acceptance model (TAM) and customers’ characteristics based on demography, psychographics traits from customer segmentation. It aims at showing the impacts of personal characteristics on the TAM in the context of mobile banking. By doing so, we would understand better the effect sizes of each factor on the process and how they impact the migration route to actual switch, in order to optimize the allocation of marketing efforts and customer relationship management (Kushwaha and Shankar 2008).

We particularly address these issues within banking sector that differ to the similar empirical studies in retails to provide stronger evidences within the profile of banking industry. Furthermore, there is no precedent studies specifically investigating the migration from conventional/online channels to mobile channel (mobile applications). Since multichannel marketing is all about choice according to Schoenbachler and Gordon (2002), in other words, when customers adopt a new technology, they are actually choosing between new technologies or old platforms. This substitution effect of new technology was ignored in most of the technology acceptance studies. In this empirical study, migration behavior will be verified. In addition to carry on the research direction from the antecedences of TAM, we will add a new factor such as usage intensity, which we assume will have a potential effect on the user’s acceptance to mobile banking applications. And this exploration will be addressed in extra findings of this study.

The research embedded in this paper aims to provide the answers for the following question:

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“What factors will trigger customers’ migration (switching behavior) from conventional/online banking channel to mobile banking applications? And how can we

influence this migration?”

While, other studies with similar approach listed in Table 3 have significant impacts on our underlying conceptualization of mobile banking adoption, it can be seen that no study has yet tried to incorporate the influences of psychographics traits to TAM (technology acceptance model) theory. Even the study about IDT extension (innovation diffusion theory) from Mattila’s (2003), only a cursory discussion of “attitude towards innovation” was found as an explanation for “compatibility”, one of the attributes of Innovation. Moreover, it is still unknown if the variables “intension to switch” and “actual switch” discussed in migration from conventional banking to online banking introduced by Lee, Tsai & Lanting (2011) would be applicable for our study about mobile banking application. Last but not least, most of studies have ignored the demographics factor which might lead difference in behavioral intentions in mobile domain (Shankar & Balasubramanian, 2009) as well as the usage intensity in the whole adoption process (Lin, 2011)

Table 3. Prior research overview

Articles Consumer Behavior Research Setting Empirical/

Theoretical IDT PEOU PU IS AS PT IU DEMO

Mattila (2003) Adoption mobile banking Empirical √ √ √ – – √ – –

Shaikh & Karjaluoto (2014) Adoption mobile banking Conceptual √ √ √ – – – √ √

Lee, Tsai, Lanting (2011) Switch online banking Empirical – √ √ √ – – – √

Gu, Lee, & Suh (2009) Behavioral intention

mobile banking Empirical – √ √ – – – – √

Lin (2011) Adoption mobile banking Empirical √ √ √ – – – √ –

Yen, & Wu (2016) Adoption mobile banking Empirical – √ √ – – – √ √

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IDT= innovation diffusion theory, PEOU= perceived ease of use, PU= perceived usefulness, IS= intention to switch (dependent variable), AS= actual switch (dependent variable), PT= psychographics traits, IU= intensity of usage, DEMO= demographics

In summary, a research gap on the psychographics variables on TAM extension are identified in the context of mobile banking, which is illustrated in the Figure 5.

Figure 5. Research objectives

4. Conceptual framework and hypotheses

4.1. Conceptual Model

The chart below properly illustrates our proposed conceptual model: Figure 6. Conceptual model

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University of Amsterdam, Meixin Zeng 10086188 Page 25 This model suggests that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) have positive impacts on Intention to Switch (IS) and Actual Switch (AS) respectively. Suggested by antecedents of TAM, behavioral intention has significant positive influence on actual behavior. Our conceptual model follows the same experiment and verifies that switching intention (IS) has positive impacts on Actual switch (AS) in the context of m-banking applications. Furthermore, we add new psychographics variables: Time Pressure (TP), Motivation to Innovativeness (MI) and Price Consciousness (PC) as extensions to TAM. We expect a moderation effect of psychographic variables on the relationship between PU/PEOU and Intention to Switch (IS). Additionally, Intensity of Usage (IU) is assumed to moderate the relationship between Intention to Switch (IS) and Actual Switch (AS). Lastly, the impacts of the covariates – education, ages and gender on Intention to Switch (IS) is also considered. 4.2. Hypotheses

This section provides an overview of all research hypotheses. The Table 4 shows the research hypotheses summary at the end of this section.

4.2.1. TAM indicators

This theory asserts that perceived usefulness and ease of use are fundamental determinants of system adoption and usage. In TAM, both of changes people’s Perceived usefulness (PU) and Perceived ease-of-use (PEOU) influence the user attitude and intention towards using, then the final use decision were taken place (Davis 1989). TAM is extended in this study to include the impacts of psychographics traits such as time pressure, motivation to innovativeness and price consciousness. Thus far, the extension of TAM to include the attitude and behavioral intention to switch has not yet been explored, and is therefore worthy of investigation.

Perceived usefulness

As mentioned above, the perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989). There are several traits found within this parameter: the perceived attributes of

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University of Amsterdam, Meixin Zeng 10086188 Page 26 adoption decision. (Davis et al. 1989; Agarwal & Prasad 1999; Porter & Donthu 2006; Gu, Lee & Suh, 2009). Relative advantage, compatibility, trialability and observability are positively related to adoption of an innovation and the remaining two, complexity and perceived risk (Lee, Tsai & Lanting, 2011).

In our study, perceived usefulness regards to the necessity of switching to mobile applications. Previous studies have underlined the role of the perceived usefulness on attitudes towards use (Davis et al., 1989, Venkatesh and Morris, 2000 and Gu, Lee & Suh, 2009). Davis (1989) argues that individuals tend to undertake behaviors they believe will help them perform their job better and more efficiently. In switching decisions, an empirical study in the fields of switching behaviors in online banking sector also confirms that Perceived usefulness has a significant and positive effect on a user’s attitude towards switching to online banking (Lee, Tsai & Lanting, 2011).“When users consider online banking apps useful, they will hold a more positive intention towards switching from a physical channel into online banking”. Meanwhile, as the banking apps are commented as useful as mentioned above, the similar intention of switching to mobile banking apps may also emerge. Therefore, we assume:

H1 a: Perceived usefulness (PU) has a positive effect on customers’ intention towards switching to mobile banking apps (IS).

H1 b: Perceived usefulness (PU) has a positive effect on customers’ actual switch of using the mobile banking apps (AS).

Perceived ease of use

Perceived ease of use is one of the most important determinants of the TAM model (Davis et al. 1989). Furthermore, Lee, Tsai & Lanting (2011) mention in her empirical study that if consumers perceive a service to be easier and more convenient for them to use, then it will directly influence the whole switching progress. It means a product or service is perceived to be easy to use has a higher chance of affecting customers’ switching process to mobile banking apps. The last but not the least, the above mentioned survey (CFCA, 2014) reveals

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University of Amsterdam, Meixin Zeng 10086188 Page 27 the mobile banking apps are commented as “swift” and “efficient”, Consequently, we can conclude the following hypothesis:

H1 c: Perceived ease of use (PEOU) has a positive effect on customers’ Intention towards switching to mobile banking apps (IS).

H1 d: Perceived ease of use (PEOU) has a positive effect on customers’ actual behaviors of switching to use mobile banking apps from other banking channels (AS).

Intention to Switch

In the context of technology acceptance, usage pertains to the utilization of technology to perform a certain task (Autzen 2007), while switching refers to the tendency or intention to exchange or shift from one method to the other. As several empirical studies (Gu, Lee & Suh, 2009) mentioned, the intention to switch is the major driver of the actual switch decision. Therefore, a hypotheses regard to the switching intention is formed:

H2: Intention to switch (IS) from other banking channels triggers consumers’ actual switch behavior for mobile banking apps (AS).

4.2.2. Psychographic covariates

Psychographic trait is defined as the characteristics of individual that exert pervasive influence on a board range of trait-relevant response. The different kinds of responses can be considered a manifestation of an underlying characteristic (Ajzen, 2003). Therefore, behavioral responses can reflect the psychographic traits of the person; in other words, psychographic traits will have impacts on the consumer behaviors. As discussed by Konus et al (2008) in the multichannel context, we can infer that shifting from a channel in use to a new channel, in this study, from other banking channels to mobile banking apps can be perceived as a customers’ adoption or behavior react to innovation (Kleijnen et al. 2007). Personal characteristics of a consumer have found to be significant predictors of innovation adoption (Sulaiman et al., 2007). Moreover, Konus et al. (2008) even point out directly that the psychographic constructs of a multichannel shopper, such as his price consciousness, time pressure, loyalty, motivation to conform innovativeness and shopping enjoyment have

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University of Amsterdam, Meixin Zeng 10086188 Page 28 significant impacts on the channel switching and migrations. Thus, the inclusion of psychographics covariates becomes highly important as basis to the switching behavior that this paper pursues. This paper will bridge the theories between the consumer segmentations and the TAM in banking sector. From the variables selected from Konus et al (2008), this paper focuses on three utilitarian variables: innovativeness time pressure and price consciousness that might exert moderation effects on the relationship between the perceived ease of use (or perceived usefulness) and the intention to switch.

Time pressure

We adopt the definition of time pressure as the extent to which the customers feel pressured to make decisions quickly (Kleijnen et al, 2007). Time pressure stimulates a more favorable attitude of consumers towards online channel rather than offline channels (Xu-priour, Cliquet & Fu, 2012). In the empirical study by Scheer et al (2010), the customer may doubt the established relationship with existing suppliers and continue to look for alternative choices under the time pressure (Scheer et al, 2010). That is to say, customers are more likely to look into other channels out of the existed chosen channels under time pressure, which is why we believe that switching behavior is more promoted among people with characteristics of time pressure. Mentioned by Suoranta (2003), mobile banking services are more valued than ever by users because of the inherent time and place independence, and the overall effort-saving qualities. And time-efficiency has been remarked as the major characteristic of mobile banking apps. As Yang and Kim (2012) also state in their empirical study, the free from wire connection allows the mobile shopping preferred by high time-pressed customers. Furthermore, a moderation effect of time consciousness on the relationship between mobile channel value perceptions on behavioral intentions is proposed and verified by Kleijnen et al (2007). Therefore, we assume time pressing also play a moderation role when multichannel switching intention happens in the TAM. Hence, the following hypothesis is developed:

H3 a: The positive effect of perceived usefulness (PU) on consumers’ intention towards switching to m-banking apps (IS) is pronounced more for those consumers who are under time pressure (TP).

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H3 b: The positive effect of perceived ease of use (PEOU) on consumers’ intention towards switching to m-banking apps (IS) is pronounced more for those consumers who are under time pressure (TP).

Motivation to conform Innovativeness

Innovativeness refers to the degree which a person prefers to try new and different products and seek out new experiences (Midgley and Dowling, 1978). Mobile apps evoke perceived association with IT, computers and electronics products (Chaffey, 2016). Konus et al (2008) pointed out that consumers with motivation to innovativeness were proven to have higher possibilities in exploring and using new alternatives. Yang (2012) found that innovative consumers are more likely to engage in mobile channels, as it may perceived as an fresh environment to explore products, services or mobile features. Similar results are found in other empirical studies, for example, Malhotra & Malhotra (2013) conducted an empirical study by group interviews and surveys to explore the switching behavior of mobile service customers in the USA with innovativeness as a deterrent. They found out that innovativeness of mobile service does play a determinant role in switching decision of mobile services (Malhotra & Malhotra , 2013) Innovative consumers were proven to have higher possibilities in exploring and using new alternatives. Therefore, we are going to investigate the correlation between the innovativeness in a person’s motivation to conform innovativeness and their switching process to mobile banking applications. Therefore, the following hypothesis is tested in this study:

H3 c: Consumers’ motivation for innovativeness (MI) moderates the relationship between perceived usefulness (PU) and intention to switch to m-banking apps (IS).

H3 d: Consumers’ motivation for innovativeness (MI) moderates the relationship between perceived ease of use (PEOU) and intention to switch to m-banking apps (IS).

Price Consciousness

Price consciousness is defined as “the degree to which consumers focus on paying low prices (Lichtenstein et al 1990), so a price-conscious consumer seeks to minimize the price paid for an item, which relates to savings” (Konus et al, 2008). Although Konus et al (2008) argue

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University of Amsterdam, Meixin Zeng 10086188 Page 30 that price consciousness is an insignificant factor splitting the “multichannel enthusiastic” shoppers and “store-focused” shoppers in consumer segmentation regarding to a shopping environment; we believe price consciousness will influence the degree of preference for mobile applications in banking sector.

According to the previous switching research from Lee, Tsai & Lanting (2014), a new factor - switching cost is added to the extension of TAM to explain the switching model. Tanford et al (2010) disclose in his empirical studies that the price sensitivity can be a decisive drive for the patrons to change hotels and thus their loyalty (Tanford et al, 2010). Heo and Lee (2011) also reveal the importance of price consciousness in customer satisfaction via perceived price fairness (Heo and Lee, 2011). Therefore, we infer that cost should be considered as a driver for mobile banking adoption. Thus, we believe consumer with higher sensitiveness in pricing would trigger higher intention from perceived ease of use or perceived usefulness to actual switch to mobile applications than other consumers with less price consciousness. Once the customer feel unfair about the bank charges of other bank channels, they are likely to switch to mobile apps, which might be promoted by free of charge on basic transactions (e.g, bank transfer, bill payment) (Boc.cn, 2016). 2 hypotheses are made in reflection to the discussions above:

H3 e: Consumers’ price consciousness (PC) enhances the positive effect of perceived usefulness (PU) on their intention to switch to mobile banking apps (IS).

H3 f: Consumers’ price consciousness (PC) enhances the positive effect of perceived ease of use (PEOU) on their intention to switch to mobile banking apps (IS).

4.2.3. Demographical covariates

A common interest in the studies included in this review is the analysis of user demographics including age, gender, and education, to predict m-banking adoption. The impact of demographics on the adoption of mobile shopping has been extensively studied (e.g., Shaikh & Karijaluoto, 2014; Lee, Tsai & Lanting, 2011; Gu, Lee & Suh, 2009; Yen & Wu, 2016). According to Crabbe et al. (2009), demographic factors play a significant role in adoption decisions. They find that social and cultural factors including the demographic factors had

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University of Amsterdam, Meixin Zeng 10086188 Page 31 significant impacts on the m-banking adoption behavior in Ghana. Similarly, the research of Sulaiman et al. (2007) reveals that both demographic and psychographic variables affect the adoption of m-banking and its applications. Teo (2011) also extends the TAM with four demographic factors (gender, age, education, and income) to predict intentions to adopt m-banking in Malaysia. Their results indicate that education and income had positive correlation with PU, while gender and education exerted positive impacts on PEOU. The following section offers some explanations of the three demographical covariates that will further investigated in this study.

Education: Some studies mentioned above suggest that Internet users hold higher educational degrees. Likewise Konus et al. (2008) illustrate that the relationship between the education level and multichannel behavior is explained by the learning skills and the capability to optimize the usage from the different channels specified in younger people. Hence we expect a positive impact of education on intention to switch to mobile banking service.

Age: Venkatesh and Morris (2000) suggest that gaining a better understanding of age differences is important, as it relates to user acceptance and usage of new technologies. Early adopters of new products or service are commonly seen to be young. According to Polatoglu and Ekin (2001), they describe electronic banking services adopters are most likely to be young and highly educated. Similarly, a Finnish study (Matilla et al., 2003) reports that the internet banking users are middle aged, relatively wealthy and highly educated. According to Chong (2013) there is a significant and negative relationship between mobile commerce use and age. This research will verify the influence of age in the switching intention for m-banking applications.

Gender: Prior research has investigated the role of gender in mobile commerce with contradicting results. Okazaki and Mendez (2013) found that men are more active than women to engage in online environment. However, the same study also finds that interface aesthetics and ease of use are the motivations of engagement in mobile commerce for

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University of Amsterdam, Meixin Zeng 10086188 Page 32 females. According to Wan et al. (2005), gender has the least useful dimension for market segmentations in the internet banking sector.

H4 a: People with higher education level will be more likely to switch from other banking channels to m-banking application.

H4 b: Age has an impact on switching to mobile banking apps usage.

H4 c: Gender affects actual switch from other banking service channels to mobile banking apps.

4.2.4. Intensity of Usage

Kim, Xu, & Koh (2004) argue that the effects of the antecedents of the adoption of retail websites for potential adopters and experienced users might be differentiated. Marler, Fisher, & Ke (2009) have identified the differences between the determinants of initial adoption and subsequent continued usage of employee self-service technology. The above discussions have motivated this study to focus on the usage intensity of consumers based on the extent of their experience with mobile banking. Empirically, few studies have examined whether the antecedents of attitude toward mobile banking differs between potential and repeat customers (Lin, 2011). Therefore, this paper included the consumers with and without any experiences with mobile banking apps and will measure the impacts of usage intensity.

Intensity of app usage refers to the frequency of mobile banking apps usage in our empirical study. An empirical study on the online service switching behavior mechanism reveals that the accumulated satisfaction is dynamically with the frequency of usage. The more times the customer use a certain channel, the greater satisfaction to the service accumulates, consequently, the customers with more previous satisfaction are more willing to switch from other conventional channels to it (Keaveney and Parthasarathy, 2001). Moreover, another research (Junco, 2012) shows that the expressive use of other functions of the internet (such as reading blogs, creating web pages, emailing, etc) are positively related to the adoption of social networking website. As mobile banking apps also shares the swiftness and serves as the express of conduct banking services (Strom et al, 2014), we may well consider the similar effect of intensity of app usage on switching decision. As a result, we hypnotize that:

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H5: Higher Intensity usage (IU) motivates the relationship between consumers’ intention to switch (IS) and actual switch to mobile banking apps (AS).

Table 4. Research Hypotheses summary

H1 a: Perceived usefulness (PU) has a positive effect on customers’ intention towards switching to mobile

banking apps (IS).

H1 b: Perceived usefulness (PU) has a positive effect on customers’ actual switch of using the mobile banking

apps (AS).

H1 c: Perceived ease of use (PEOU) has a positive effect on customers’ Intention towards switching to mobile

banking apps (IS).

H1 d: Perceived ease of use (PEOU) has a positive effect on customers’ actual behaviors of switching to use

mobile banking apps from other banking channels (AS).

H2: Intention to switch (IS) from other banking channels triggers consumers’ actual switch behavior for

mobile banking apps (AS).

H3 a: The positive effect of perceived usefulness (PU) on consumers’ intention towards switching to

m-banking apps (IS) is pronounced more for those consumers who are under time pressure (TP).

H3 b: The positive effect of perceived ease of use (PEOU) on consumers’ intention towards switching to

m-banking apps (IS) is pronounced more for those consumers who are under time pressure (TP).

H3 c: Consumers’ motivation for innovativeness (MI) moderates the relationship between perceived

usefulness (PU) and intention to switch to m-banking apps (IS).

H3 d: Consumers’ motivation for innovativeness (MI) moderates the relationship between perceived ease of

use (PEOU) and intention to switch to m-banking apps (IS).

H3 e: Consumers’ price consciousness (PC) enhances the positive effect of perceived usefulness (PU) on their

intention to switch to mobile banking apps (IS).

H3 f: Consumers’ price consciousness (PC) enhances the positive effect of perceived ease of use (PEOU) on

their intention to switch to mobile banking apps (IS).

H4 a: People with higher education level will be more likely to have intention to switch from other banking

channels to m-banking application.

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University of Amsterdam, Meixin Zeng 10086188 Page 34 H4 c: Gender affects actual switch from other banking service channels to mobile banking apps.

H5: Higher Intensity usage (IU) has an impact on intention to switch to mobile banking apps usage.

5. Methodology

Our empirical study focuses on the TAM relationship in the switching decision to mobile banking apps and the influences of several customer personality characteristics. Therefore, our empirical study mainly imitates the previous study of Lee, Tsai & Lanting (2011), which the switch decision from conventional channels to online banking by using TAM model are considered. Our data are collected via survey, and the results will be analyzed by SPSS. This section starts with the explanation of research approach and statistical models in the research, then the details of our sampling and measurement will be presented and discussed. An overview of the results of factor analysis will be provided prior to process the data analysis in the next section.

5.1. Analytical strategy and procedures

The hypothesis and theoretical framework is summarized as the chart in Figure 6. The underlining assumption of TAM, the intention to behavior association, and the influence of perceived usefulness (PU) and perceived ease of use (PEOU) will be tested by our data first. Moreover, as previous literatures (Konus et al, 2008; Lee, Tsai & Lanting, 2011; Malhetra et al, 2013) suggested, several psychographics variables, such as time pressure (TP), motivation to innovation (MI), price consciousness (PC) are also have ineligible impact on the TAM relationship, so those variables are also included in our empirical study and thus their interaction with the switch decision will also be tested.

To perform the statistical analyses, SPSS 22.0 (Mac) was used. After recoding the reverse coded item in perceived ease of use (PEOU), the means of each item was computed. Scale reliabilities, descriptive statistics, skewness, kurtosis and normality were subsequently tested. All items among the constructs were tested under the control of demographic variables (gender, age and education) by using ANOVA. The mean scores of the items should be

Drivers of Mobile Banking Behavior and the use of M-banking applications : an empirical study based on Technology Acceptance Model (TAM)