Drivers of Mobile Banking Adoption in Turkey
Faculty of Business and Economics
MSc Business Administration – Marketing Track
Master’s Thesis
Deniz Cavusoglu
10824545
Supervisor: Dr. Umut Konus
Date of Submission: 27 June 2015
STATEMENT OF ORIGINALITY
This document is written by Deniz Cavusoglu who declares to take full
responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and
that no sources other than those mentioned in the text and its references have
been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision
of completion of the work, not for the contents.
TABLE OF CONTENTS
Abstract………....………..…...………...………...5
1) Introductıon………..………...…………...6
2) Literature Review………...………...9
2.1) Mobile Commerce and Shopping………..………...9
2.2) Mobile Shopping Drivers & Adoption……….………..10
2.3) Mobile Banking……….………...11
2.4) Mobile Banking Drivers………...13
2.5) Current Situation in Turkey………..……….17
2.6) Gaps and Research Question……….……….19
3) Conceptual Framework………..……….…21
3.1) Mobile Banking Adoption Levels………...……22
3.2) Mobile Banking Adoption Drivers……….23
3.2.1) Values and Lifestyle………...…….23
3.2.2) Benefits and Concerns……….24
3.2.3) Control Variables………....25 4) Hypotheses………..……….……….………26 5) Research Design………..……….………31 6) Results……….…...……….………..………32 6.1) Preparations………...……….……….………32 6.2) Sample Descriptives……….………...….………33 6.3) Reliability……….………34 6.4) Hypotheses testing……….………...….…………..……34
7) Discussion & Conclusion……….………....………38
8)Managerial Implications……….……….…………...………….………41
9) Further Research……….……...……….………43
10) Appendix……….44
Acknowledgement
I would like to thank my supervisor, Dr. Umut Konuş for his guidance and support throughout my process of writing this Master’s Thesis. His experience, knowledge and patience had been my greatest advantage since the beginning. I would not be able to achieve my Master’s degree without him. Furthermore, I would like to thank my mom, dad and my sister for being there when I needed. I would not be able to achieve anything in life without my family.
I hope you enjoy reading this thesis, Deniz Cavusoglu
Abstract
This Master’s thesis was written under the supervision of Dr. Umut Konus for
University of Amsterdam, Faculty of Economics and Business. The research tries to find out, relate and prove the possible mobile banking adoption drivers in Turkey. A quantitative research based on survey methodology was conducted. The data used for the analyses were gathered through an online survey with approximately 500 participants, which was designed and conducted specifically for this research. The tested hypotheses were proposing the effects of psychographics and behavior on the mobile banking adoption. The proposed effects of innovativeness, impulsiveness, modernity, privacy concerns, financial benefits, number of banks worked with and number of financial services used were tested as well as the
moderating effect of time pressure or demographics like gender. As a result, three of the eight hypotheses proposed were supported. Among these, innovativeness and number of financial products used were supported to have an effect on behavioral adoption and impulsiveness was supported to have an effect on initial adoption. Discussions, managerial implications and limitations of the results and further research directions conclude this research.
1.Introduction
In this era of technology and innovation, Internet has been the base of almost every activity in our lives. Especially for the activities that we don’t really need to be physically present during the process such as banking, it is more convenient for most to conduct our routines like paying bills or sending money through Internet. That’s why, the number of people who uses online banking has been increasing globally as expected. (Ahmad & Al-zu’bi, 2011) Not only for banking, takeover of the online channels has been empowering its presence also in shopping, transaction systems or security verifications such as Albert Heijn online grocery, PayPal payment systems or online check-ins for airlines. When the online process is much more faster and convenient for all parties, both customers and organizations encourage the adoption. Moreover, particularly after 2010, with the rise of smartphones, mobile channels has become the most popular way of online transactions as we can see in the Table1 below. Now, many people prefer using their tablets or smartphones on the go for their monetary activities. But what is the reason behind this trend? What are the reasons that make people choose online transactions over the real
payment? In the article “The impact of consumer Internet experience on channel preference and usage intentions across the different stages of the buying process” Frambach et al.
(2007) states that “consumers
prefer the offline channel over the online channel in the purchase stage”. However, in the article “Hedonic and utilitarian motivations for online retail shopping behavior”, Childers et al.(2001) underlines the convenience factors like efficient time management, not having to carry cash or cards, personal preferences on social avoidance and being able to use one screen for all needs so consumers can avoid traffic jams, shop from stores all around the world 24
hours/7 days. But these reasons seem to differ in mobile banking compared to other mobile transaction systems. Despite the fact that time efficiency and not having to carry cash are the big pluses for mobile banking as much as it is for other transactions, effect of privacy
concerns is proven to be much higher for banking. Bart et al.(2005) states that by saying “the influences of the determinants of online trust are different across site categories and
consumers. Privacy and order fulfillment are the most influential determinants of trust for sites in which both information risk and involvement are high, such as banking” for instance, people would like to make sure their money is safe and preferably talk to someone when they open a savings account for their retirement, unlike buying a coffee on the go to take it away from the Starbucks that is two blocks ahead. People’s motivations for mobile banking are significantly different than their motivations for mobile shopping.
Today, More than a third (37%) of internet users surveyed in European countries are already using mobile banking (ING Bank press release, 2013) Northern European countries are among the most advanced ones in the adoption to and use of different new mobile and technological appliances and these countries have extended the implementation of technological advancement in banking services (Mattila, 2003) With the increasing number of cell phone users, 8.5% of all cell phone users
throughout the world have used mobile banking. (Gu et al., 2009) As banking branches get closer to their saturation point in growth and even shrinking in countries like Finland, mobile channels are the hot prospect for the future. On the other hand, if mobile banking is the future of banking and clearly the most convenient way;
often or never? Culture, situation of economy and shopping habits are the main drivers that create the variation in adoption level of mobile banking for different countries. Emerging markets like Turkey, for instance, are among the few countries that have higher transaction number and volume than European countries. “The total number of registered customers that logged in at least once was 3.6 million as of June 2013, where 2 million of them (56 percent of total customers) used mobile banking services during the April-June 2013 period. The total number of registered customers that logged in at least once in one-year period was 2.7 million. The total number of investment transactions performed by using mobile banking services was 1 million with an amount of TRY 6.9 billion as of June 2013” as stated by The Banks Association of Turkey in the yearly report of Internet and mobile banking statistics.
My research will try to answer the question, what is it that makes a bank capable of successfully implementing its mobile banking channels in a country but not in another? As you will see in the literature review part, the literature has several research papers that work on these questions globally or specifically on the other emerging markets like China or India. But there is rarely enough research in literature for niche markets like Turkey. Moreover, behavioral elements of mobile banking usage have been studied rarely despite the fact of literature needed on the issue. The importance of up-to-date papers on the topics driven by technology and trends is higher than the less-dynamic topics that has already been studied deeply for longer years. The research topic adoption drivers of Mobile Banking in Turkey, fills this gap in the literature, both in the sense of the topic itself and its focus area. One of the other reasons that make my research topic interesting is, as the sixteenth biggest economy in the world, Turkey has a remarkable position among developing countries. (Tuncel, 2014) various customer segments with the strong effect of cultural and social drivers, makes Turkey an attractive focus country to study. The possibility of concluding unique and useful findings at the end of my study is high. Spotting the correct drivers is useful for strategic planning for
practitioners in order to relocate the resources accordingly, specify and target customer segments. Managers and/or researchers can use the expected finding of this study in:
• Resource allocation
• Marketing communication optimization • Strategic planning
• Content development and design of mobile banking applications.
The main concepts in my thesis will be the usage level and magnitude of mobile banking in turkey, cultural and personal aspects as well as personal characteristics that affect the mobile banking adoption. The general structure of my thesis will be based on the existing literature on the topic, a survey that I will conduct and the analysis of the results that I will acquire from the survey.
2.Literature Review
2.1 Mobile Commerce and Shopping
Mobile Commerce (m-commerce) is known as an extension of e-commerce and refers to any transactions, either direct or indirect, with a monetary value, implemented via wireless telecommunication network. (Kleijnen, Ruyter & Wetzels,2007) These transactions include banking, investing, auctions, shopping and mobile phone services and are considered as a separate channel by marketers as they offer convenience and accessibility at any time and any place. Mobile shopping, however, is defined as the activities of consumers who use wireless Internet service when shopping and purchasing goods and/or services via a mobile phone or a tablet. (Ko et al. 2009) Mobile phones and tablets are the personal devices for customer to access any form of mobile shopping such as organization-based applications that are designed
and launched by the companies providing mobile shopping service. eBay’s mobile application is an example of that. Mobile phones and tablets’ web browsers are also a common form of mobile shopping. Accessing to web pages of the providers through the browsers is the form of m-shopping that enables customers to reach multiple providers, independently. One of the reasons that mobile shopping is globally booming for the past 10 years is the customer’s convenient usage of mobile channels at any usage phase such as information search, transaction or after sales. Using any of the devices and forms of m-shopping stated above, customers can search, compare, consult, purchase and track their transactions for any service or product with less time, effort and money.
As mobile commerce volume has been growing globally with the help of the Internet, more and more players started to be interested in the topic. Especially, with the trends in mobile shopping nowadays, it’s critical for marketers to understand consumer motivations that lead them to individual adoption intentions. According to Ernst & Young’s 2014 press bulletin, by 2017 transaction volume of mobile payments will quadruple and reach 1,3 trillion dollar per year. It is also stated that in 2001, there was only one active mobile payment
system, whereas today there are 150 active mobile payment systems and 90 more are pending.
2.2 Mobile Shopping Drivers & Adoption
These motivations can be generalized under the name of drivers as the causes to adopt the new and innovative way for consumers’ purchasing activities. As we study these adoption drivers, we classify our drivers according to prior literature.
The theory of reasoned action (TRA), proposed by Fishbein and Ajzen(1975), is a model that has been used broadly to predict and explain human behavior in various domains. Derived from TRA, Davis (1989) proposes the Technology Acceptance Model (TAM) to
explain how users come to accept and use a technology. Gefen (1997) suggests that TAM also applies to e-commerce and the two most important drivers are perceived usefulness and perceived ease of use. (Gefen & Straub, 2000) However, many researchers suggested that TAM from Davis (1989) needed additional drivers to provide an even stronger model. (Wu & Wang, 2005) Thus, Venkatesh and Davis (2000) proposed an extension, TAM2, which includes social influences to adoption drivers. Therefore, as we will explain in detail in the conceptual framework part, we will study adoption drivers from both dimensions, personal and social.
Personal adoption drivers refer to the consumers’ personal traits that affect the level of behavioral adoption of a new technology, product or a service. For instance, we can give the example of a person’s innovativeness or perceived benefits increases perceived usefulness of mobile shopping, therefore increases the likelihood of adoption. On the other hand,
educational or psychological traits of a person would be considered socially influential drivers that affect the adoption.
2.3 Mobile Banking
“The internet banking phenomenon has transformed the way banks across the world carry out banking transactions and has brought about new strategic directions for investment in banking information and communication technologies.” (Sabi, 2014) Moreover,
Technologies that have been developed since the invention of the Internet has created new channels for Internet banking and mobile banking has emerged as a wireless service delivery channel providing increased value for customers' banking transactions (Laukkanen, 2007) Today, Mobile banking (m-banking) is one of the most popular online channels for banking
that enables customers to be able to process their online banking transactions through their mobile devices.
However, creation of a conventional or radically innovated channel doesn’t guarantee that it will be fully implemented and used broadly; especially for a hard-to-trust sector like banking. Although first usage of mobile banking dates back to 1999, the usage of mobile banking was limited to receiving and sending SMS and rare banking platforms offered for phones with WAP support until 2010s. With the introduction of smartphones, Mobile banking has started to evolve into its shape it has nowadays. According to Ernst & Young’s press bulletin in 2014, the number of people who use mobile banking will double today’s number and exceed 1 billion by 2017.
According to Dow Jones statistics provider Statista, mobile banking penetration in
selected European countries in 2013 and 2014 is shown in the Figure2 aside. Turkey is the first in the m-banking penetration rankings in Europe with 56% penetration rate.
This dynamic evolvement on its
own creates the need and lack of up-to-date research to analyze dialectics of the sector. The studies to be conducted on mobile banking have to be frequently updated, dynamic and responsive to world trends to keep up with the technological developments that are going on continuously. More importantly, Singh (2011) in his paper ‘innovated technology in banking
services’, states that in order to analyze and improve usage period, customer satisfaction and
adoption rate of mobile banking there are other measures than technology and infrastructure itself.
2.4 Mobile Banking Drivers
Today, The demand on the usage of online channels is not sufficient and below expectations for most of the countries. (Tchouassi, 2011) but what causes this insufficiency? Or what are the drivers that should be focused on to improve the usage of mobile banking by customers? Tchouassi, in his paper ‘Can Mobile Phones Really Work to Extend Banking
Services to the Unbanked? Empirical Lessons from Selected Sub-Saharan Africa Countries’
works on this question demographically and states that as the usage of smartphones and mobile payment increases, m-banking will improve its importance and become better widespread among Sub-Saharan Africa countries. But, how about the countries that already have high smartphone and mobile payment usage rates? What are the different drivers that affect the usage and adoption rate of mobile banking? Donner & Tellez (2008) relates this to the economic development of the country in the paper ‘Mobile banking and economic
development: Linking adoption, impact, and use’. The authors state that main affective
drivers of mobile banking would be studied in developed countries because as Cracknell (2004) says “For users in the developing world, on the other hand, the appeal of these m-banking/m-payments systems may be less about convenience and more about accessibility and affordability”. However, according to the paper ‘Mobile Banking in India: Practices,
challenges and security issues’ by Goyal, Pandey & Batra (2012) there are currently 225
million mobile phones in India and it is expected to exceed 500 million by 2015. That means there is a growing opportunity for mobile banking also in developing countries that can’t be neglected. According to the international assurance and consulting company Ernst & Young’s press bulletin of 2014, developing countries are holding a great prospect for mobile banking and given the example of Turkey with a 190% increase in financial transactions through mobile banking channels in the past year. As a result, there are different views in the
differences about the focus groups of different studies and their different impacts are among the main factors that create the research gap on this topic.
Apart from the anthropological drivers that might limit the adoption, another question to be addressed in the literature about mobile banking should be what is the reason behind the success of mobile channels; especially the rise in Asia-pacific region whereas development of mobile channels in North America and Europe has not been as successful. This could be the key to improve the usage of mobile channels in developing countries. “The main difference between successful implementations in countries like South Korea, Japan and failure in Europe and North America is primarily attributed to the payment culture of the consumers that are country specific.” Says Goral et al. (2012). This brings up the question about how to inspect and analyze the mobile channel usage based on consumer behavior on different countries. Because if there is any cultural or economic framework that we can use to
anticipate consumer behavior in different environments that would significantly improve the literature of marketing and its applications on mobile channels.
The answer to these questions lay behind the fact that mobile banking drivers shouldn’t be studied with the same perspective that other mobile channels’ adoption drivers are studied. Mobile banking, as a sensitive topic that merges two topics that most consumers approach with caution, namely technology and banking, has a strong influence from the consumers’ trust factor. Moreover, the payment culture that was stated above varies even more on banking from country to country. People don’t need to be as impulsive as they pay their bills on the last minute from their mobile devices when they are deciding to which television to buy from Mediamarkt’s online store. They don’t have the time pressure of sending the money to their landlord before the first day of the month as they order pizza from the Domino’s app. Neither nobody risks their lifetime investment when they buy a flight ticket through an untrusted network. According to Ernst & Young’s 2014 press bulletin, 68%
of the smartphone users are not using mobile banking due to the security concerns. Moreover, McKinsey & Company’s report on mobile banking dating April 2013, states the insufficient usage yet promising future of mobile banking as; “Banking through the mobile handset is lagging behind other types of on-line sales. However, it can become the preferred channel if banks address the security concerns of customers.” (Jayantilal et al. 2013) As it will be elaborated more later on, security concerns of customers are among strongest drivers for mobile banking adoption for most. Figure3 shows the increase in sales on non-banking products on mobile compared to the mobile banking. The extreme importance of security concerns, as a driver in mobile banking compared to other mobile channels is remarkable. Therefore, my study looks at the mobile banking drivers from a unique perspective to understand and model specific
drivers of m-banking that have been overlooked so far such as, demographics, trust factor, psychological and personality traits.
A good example of a similar research question was studied in the article ‘consumers’
attitude towards online and mobile banking in China’ by
Laforet & Li (2005); the authors
states their findings as, “The results showed Chinese online and mobile bank users were predominantly males, not necessarily young and highly educated, in contrast with the electronic bank users in the West. The issue of security was found to be the most important
factor that motivated Chinese consumer adoption of online banking. Main barriers to online banking were the perception of risks, computer and technological skills and Chinese
traditional cash‐carry banking culture. The barriers to mobile banking adoption were lack of awareness and understanding of the benefits provided by mobile banking.” As the second biggest economy in the world, these findings on consumer behavior in China shows that economic frameworks or infrastructural measures are not always enough to explain all the consumer drivers. Similarly, the article “Internet banking adoption strategies for a
developing country: the case of Thailand” by Jaruwachirathanakul & Fink (2005) analyzes
Thailand as the paper states their purpose as; “identify the unique factors that encourage consumers to adopt internet banking services in Thailand and to use the study's findings to develop strategies for banks on how to maximize the rate of adoption.” However, factors identified by Jaruwachirathanakul & Fink were different compared to China; the factors that encourage the Internet banking adoption in Thailand most are ‘Features of the web site’ and ‘Perceived usefulness’. However, the most significant obstacle to adoption is a perceived behavioral control, namely ‘External environment’. There are also significant moderating factors such as gender, educational level, income, Internet experience and Internet banking experience, but not age. (Jaruwachirathanakul & Fink, 2005) As a result, countries with strong effect of cultural drivers may vary unexpectedly in the sense of customer behavior on mobile banking, such as Turkey. As found in the current literature, country’s development level and size of the economy also affects the adoption process of mobile banking.
2.5 Current Situation in Turkey
In the article ‘adoption of internet banking among sophisticated consumer segments
in an advanced developing country’ by Akinci et al. (2004) Turkey is defined as an advanced
developing country. At this point, it is essential to understand what are the varying drivers of mobile channels depending on the country; because as another paper on the same topic, the article ‘internet banking market performances: UK versus Turkey’ (Sayar & Wolfe, 2007) states that “It is found that Turkish banks offer a wider range of services from their internet branches compared to British banks, despite the fact that the UK has a more favorable environment for internet banking in terms of the level of sophistication of its banking sector and technological infrastructure.” However, Online channel usage rates are still higher in the UK so, can we say that country specific payment culture we mentioned above is a driver that is more important than the level of technological adoption in developing countries? Where is the break-even point for culture to overcome convenience as a driver for the usage of mobile banking channels? Do the findings from Turkey match with the other research papers
findings across the world? What are the possible future research topics on the issue?
According to Deloitte’s report ‘Turkish Banking Sector Outlook 2015’ Turkish financial system, especially the banking sector has been focused by developed countries due to the promising growth and higher rates of return than in developed countries. Deloitte cites EIU (The Economist Intelligence Unit) for the forecasted growth of 12% for the next 4 years. Today, according to The Banks Association of Turkey’s March 2015 report, there are 37 million 320 thousand people that are actively registered for online banking in Turkey. 38 percent of the individual bank customers have used online banking in the past three months, whereas this number is only 6 percent below of the European Union member countries’ total online banking usage ratio, which is reported to be 44% by Eurostat. Among 12 million 578 thousand registered mobile banking users, 68% percent of them have used mobile banking in
the past three months, while the number of users for the past year is above 10 million. Total transaction volume of the financial activities through mobile banking is reported to be above 98 billion Turkish Lira. ( ≈33 billion Euro)
As we review the current literature on the topic and formulate a research question to study, we list the research gaps in the literature. Studies in developed and developing countries don’t include the possible intermediate variations of drivers during the adoption process of mobile banking. Therefore, the fact that Turkey is one of the fastest growing economies in the world at the moment and has very few studies on its mobile banking sector makes it an attractive country to work on. Moreover, Studies on similar type of research questions vary on their focus country and Turkey is a unique choice for that. Thırdly, there are currently different classifications on drivers such as demographic, economic or culture in the literature that creates different views so, the opportunity to study deeper on the topic. As an ‘advanced developing country’ with various types of drivers for different customer types, mobile banking sector in Turkey would benefit the literature to better analyze and understand the customer drivers due to lack of study on country specific. Moreover, my research would also benefit practitioners in Turkey in the sense of targeting the customer and relocating their investment resources.
2.6 Gaps and Research Question
In summary, the importance of my research topic and the research gaps in literature that will be filled by the research question ‘what are the drivers of mobile banking adoption in Turkey and what are the factors that affect this driver-adoption relationship?’ can be stated as;
-Although, Turkey is one of the fastest growing economies compared to other developing countries, there are not enough studies on mobile banking in Turkey. Country setting is one of the main differentiations of my research.
-With regards to drivers of mobile banking channels, there are several drivers that are possibly effective on mobile banking usage but not studied on. My research is aimed to reveal some unknown and/or overlooked drivers.
-For a study that looks for behavioral effects of economic, educational, mental and cultural drivers of mobile banking, Turkey has a great variety and solid cultural background. I’m expecting to have a research sample that is rich in variety.
-My research results will also be possibly useful for global companies that would want to branch out to developing countries with opportunities. Understanding the local motivations and applying them to their strategy will help them to better adapt their strategies to different countries.
Therefore the main research question “what are the adoption drivers of mobile banking in Turkey?” would fill in the above emphasized research gap in the literature. Moreover, the research can also be used for managerial purposes as Turkey has its mobile banking opportunities in the market. Both commercial and investment banks might benefit from considering studied drivers in their future marketing strategy on their mobile banking
channels. The chart below is showing the distinguishing components and uniqueness of my research compared to the other studies on the topic.
Research Comparison
Emerging market as the focus of study
Studied Drivers of Mobile Banking Up to date (published in the past five years) Psychological Cultural Economical Educational
Singh, 2011 ✓ ✓ ✓
Tchouassi, 2011 ✓ ✓
Donner & Tellez (2008)
✓ ✓
Goral et al. (2012 ✓(India) ✓ ✓ ✓
Laforet & Li (2005); ✓(China) ✓ ✓ ✓
Jaruwachirathanakul & Fink 2005
✓(Thailand) ✓ ✓ ✓ ✓
Akinci et al. (2004) ✓(Turkey) ✓
Sayar & Wolfe, 2007 ✓(Turkey) ✓
My Research ✓(Turkey) ✓ ✓ ✓ ✓ ✓
3.Conceptual Framework
This research will be conducted in a framework in order to first analyze why countries vary in adoption level for their mobile banking application usage and what is the point that countries vary. In parallel with my framework, Hofstede & Bond (1984) establish their cultural dimensions theory based on a cross-cultural framework in their paper Hoefstede’s
culture dimensions: an independent validation using Rokeach’s value survey. This
framework analyzes a society’s cultural values and how these values relate to behavior. However, before analyzing the cultural motivations that cause the difference in behavior I will first define the different groups in behavior based on their usage of mobile banking. There will be three different main groups to be analyzed based on their adoption level; Initially adapted, behaviorally adapted, and non-adapted users. Further groupings will also be used based on their usage frequency, personal motivations and control variables etc.
Behaviorally adapted users will have two sub-divisions based on their usage level as advanced adapted and limited adapted. Initially adapted means that the person has downloaded the application on their mobile device but not using it. This group mainly
includes people who had downloaded the application as they were told when they had opened their bank account, to join a lottery or promotion as a part of bank’s marketing campaign or having the application downloaded in their devices just in case they might need it.
Behaviorally adapted group refers to the group of people who has the application downloaded and using it. This group will include limited adaptors and advanced adaptors. Advanced adaptors include people who use mobile banking for all the banking activities as long as the application allows them. For instance, opening up a savings account from the app, making investments or organizing their bank accounts is the behavioral aspects of advanced adapters. On the other hand, for limited adapters their usage is limited to their basic needs such as small transactions made through the app or paying the bills. The third main user group is
non-adapted users, which is basically the people who doesn’t have the application installed on their devices. Apart from the way people use mobile banking, it’s also important to analyze people’s frequency of usage. Behaviorally adapted participants will also be evaluated based on how often they use mobile banking. The frequency intervals are to be determined in the questionnaire for the participants. Sure enough, control variables will be as well determinants for the grouping.
3.1) Mobile Banking Adoption Levels Non-adapted
The term ‘non-adopters’ refers to the group of people who don’t have any mobile banking application installed in their devices therefore also don’t use them at all. This group also includes people who have had a mobile banking application installed previously, but not anymore
Initially adopted
Initial adoption means, having the application installed on one or multiple devices but not using them. The limit of not using is set to the below 2 times a month. This group of customers is separated from behaviorally adopted customers due to the fact that it’s expected to see a significant number of people who download and/or register to mobile banking applications as they open their bank account but not using it. Also flyers, discounts and giveaway vouchers are used as a marketing tool by banks to promote mobile banking. Therefore, it’s important to differentiate this group from behaviorally adopted customers.
Behaviorally adopted
Behaviorally adopted users are the people who have the application installed in one or multiple devices and also using them more than or equal to twice a month. This group of
people will be analyzed according to their usage length and the choice of financial service through mobile banking.
3.2) Mobile Banking Adoption Drivers
Drivers that will be studied in this research are mainly divided into two categories; personal traits that are based on the values and lifestyle of the respondents and the drivers that are based on the benefits and concerns of the respondent.
3.2.1) Values and Lifestyle Innovativeness
“Innovativeness is the degree to which an individual is earlier in adopting an
innovation than are other members of the social system.” (Blake et al. 2003) innovativeness of the respondents will be measured based on two questions from the survey.
Impulsiveness
Impulsiveness is the person’s initiative to take any action without planning much or spontaneously. (Sonuga-barke et al. 1994) the argument on more impulsive people’s mobile banking adoption will be examined through hypotheses. There will be two questions in the survey to measure the impulsiveness.
Modernity
Although modernity is such a broad term for different type of social sciences, the personal trait modernity in the marketing concept is defined as self-consciousness of the ‘new age’. It regards ‘the modern’ as new and different then ‘the pre-modern’ (Wenming, 2000) In that sense, this might be seen similar to innovativeness, but being innovative puts itself in a pioneer’s position rather than keeping up with the modernity. Also, Modernity of a person
has a deeper aspect than a person’s lifestyle, mentally and behaviorally rather than just an act or an idea as it is for innovativeness.
Time pressure
Time pressure in this concept can be defined in terms of the amount of cognitive and physical process that needs to take place in a time unit that is allotted for a fixed amount of process. (Ben Zur & Breznitz, 1981) The time pressure aspect of a respondent’s lifestyle will be measured through two questions in the survey in order to see the level of time pressure that a respondent is suffering in his life.
3.2.2) Benefits and Concerns Price & deals
Financial concerns of a respondent will be measured through this driver category. As a financial service channel, mobile banking adoption is expected to be strongly related to the financial concerns of the customer. Also, Luarn & Lin (2005) states perceived financial cost-benefit as the strongest determinant to reflect people's concerns about the knowledge and financial resources needed to use mobile banking.
Privacy concerns
Mukherjee & Nath (2003) states the consistently high levels of customer concerns about privacy and security in online activities. Luarn & Lin (2005) also emphasizes that the main concern regarding the mobile banking adoption is wireless connection security and lack of trust negatively affects the mobile banking usage. Therefore, before examining the
relationship between the privacy concerns of the customer and mobile banking adoption, the respondents’ privacy concern levels will be measured through the survey.
3.2.3) Control Variables
Demographic information like sex, age, education or location of the respondents will be gathered and used as control variables during the research. Below figure visualizes the above-defined drivers and their relationship with mobile banking adoption.
Drivers V al ue s and L if es ty le innovativeness impulsiveness Modernity Time Pressure B ene fi ts & Con ce rns
Price & Deals Privacy Concerns
Figure 4: Conceptual framework diagram
After grouping my participants based on their behavioral aspects of usage and control variables, I will also look at their motivations and the channels that they are using. As Smura et al. (2009) discussed in the paper a framework for analyzing the usage of mobile service; multiple mobile device owners behaviorally vary in their usage of different devices.
Therefore, I am also expecting to find a correlation between the type of mobile device and the usage of mobile banking. To be more specific, the user experience of a tablet is seen more similar to a computer, although it’s practically same as using your mobile. We will question if that cognitive similarity has an effect on security concerns of customers or on any other
Demographics Adoption levels 1.Non-adapted 2.Initially adapted 3.Behaviorally adapted Advanced adaptor Basic adaptor
motivation that has an impact on user behavior. Thus, we will look at users’ motivations based on the behavioral input that we have acquired. My research will study people’s motivations for using mobile banking in two main groups: user benefits and user values & lifestyle. User benefits will include the possible motivations of users based on the benefits they acquire by using mobile banking. User values & lifestyle, however, will include people’s personal characteristics and circumstances in their lifestyle. In order to inspect values & lifestyle of the users, we will look at participants’ innovativeness, modernity, time pressure & business and impulsiveness. To inspect the beneficial drivers of the subjects, we will look onto price & deal opportunities of mobile banking, privacy concerns and time efficiency of mobile channels. Based on this framework, below you can find research hypothesizes to be tested.
4. Hypotheses
H1: Innovativeness is positively associated with the behavioral adoption level.
Midgley & Downling (1978) argues in their paper “Innovativeness: The Concept and Its
Measurement” that consumer innovativeness has very little to do with the personal traits and
more to be depending on the action to be taken. Rijnsoever & Donders (2009) defines this relationship as product-specific innovativeness, which is one of the three levels of predictors of global innovativeness according to their paper. Hirschman (1980) also states that
innovativeness is one of the three main constructs of consumer behavior. Therefore, my hypothesis on participants’ innovativeness will measure specifically their behavioral adoption of mobile banking based on their innovativeness. The hypothesis is arguing that innovative people show the tendency to use mobile banking applications more in magnitude and frequency, whereas the innovativeness has no correlation reported on causing the person
download the application (initial adoption). Innovativeness is the independent variable of this hypothesis and the behavioral adoption level is the dependent variable. Therefore, we are expecting a positive X to Y relationship in this hypothesis.
H2: Impulsiveness is positively associated with the initial adoption level.
Impulsiveness is stated to be positively correlated with online shopping according to Darley, Blankson & Luethge (2010) As defined in conceptual framework, impulsiveness can be basically referred as how spontaneous a person is. Rook & Fisher (1995) defines
impulsiveness as “a consumer tendency to buy spontaneously, non-reflectively, immediately, and kinetically”
Hypothesis 2 is testing if a person’s impulsiveness is positively associated with the initial adoption of the mobile banking. Therefore, the questionnaire will measure participants’ impulsiveness level based on Hofstede’s framework as the independent variable. Then, the positive association between more impulsive participants and their level of initial adoption will be checked to see if there is a positive X-Y relationship between impulsiveness (X variable) and the initial adoption level (dependent variable, Y)
H3: Impulsiveness is positively associated with the behavioral adoption level.
According to San-Martin & Lopez-Catalan (2013), importance of impulsiveness in m-commerce is proven by the mobile service providers’ effort to attract impulsive purchases; Most of the marketing actions are designed according to impulsive behavior of consumer yet this decreases the customer satisfaction. Sun & Wu (2011) states that impulsiveness in online shopping has been explored but effect of impulsiveness in mobile shopping is relatively unknown (Wilska, 2003) Hypothesis 3 is testing if a person’s impulsiveness has also positive correlation with the behavioral adoption of the mobile banking as well as the initial adoption
(application installation). It will be expected to see positive association between more impulsive participants (independent variable) and their behavioral adoption level (dependent variable). This hypothesis is based on the idea if a person is more impulsive then, he would show acts of unplanned behavior; therefore easy access of mobile banking suits well to his daily needs like paying bills or sending money.
H4: Positive association of impulsiveness with behavioral mobile banking adoption is stronger for male participants
As one of the main control variables, sex of the research participants is believed to have a major effect on behavior. (Deaux & Major,1987) Moreover, in the article Sex Differences in
Impulsivity: A Meta-Analysis, (Cross et al. 2011) it is stated that men are overrepresented in
behaviors that are linked to impulsivity and sex differences are remarkable for activities in which men have been reported to show greater reward seeking behavior. For this hypothesis, Sex is defined as a moderator. Therefore the positive association between impulsiveness (independent variable) and behavioral mobile banking adoption (dependent variable) will be manipulated by the control variable sex to see if any significant change in the measures can be observed.
H5: Modernity is positively associated with behavioral adoption
For the hypothesis 5, modernity index of the subjects is the independent variable. In the paper “Evaluating the design of retail financial service environments”, Greenland & McGoldrick (2005) states that dimensions of modernity are particularly pertinent to financial services settings such as mobile banking channels. Moreover, Xiao & Kim(2009) explains the
modernity and behavior relationship as “T h e increased consumer modernity will have attitudinal and subjective norm effects on consumers’ purchase intentions” Similarly, this hypothesis will test
this positive association between modernity of the subjects and their mobile banking adoption, which is the dependent variable of this X-Y relationship.
H6: Privacy concern of the user is negatively associated with behavioral adoption with the moderating effect of time pressure
“Since mobile banking is relatively new electronic delivery channels being offered by banks, people may choose not to adopt mobile banking because of security or privacy concerns” (Lin, 2011) Moreover, Lee & Chung (2009) states that previous studies proves that lack of trust is one of the most frequently cited reasons for customers not to use mobile banking. The reason behind it is the greater degree of trust needed for the online transaction environments compared to face-to-face transactions. (Lee & Turban, 2001) However, time pressure over consumers is expected to create cognitive dissonance and cause tendency on choosing short-term benefits over long-short-term concerns. Therefore, Hypothesis 6 argues that the time pressure of the consumer moderates this negative association between privacy concerns and
behavioral adoption. The privacy concern index is the independent X value and the behavioral adoption is the dependent Y value for this hypothesis.
H7: Customer’s sensitivity on financial benefits is positively associated with behavioral adoption level.
Laforet & Li (2005) states that one‐third of online bank users in China adopted mobile banking, whereas the other two‐thirds heard about it but did not use it because they were not clear about its benefits. Customer awareness of financial benefits of mobile banking is one of the main drivers of adoption. Therefore the Hypothesis argues that customers who seek for financial benefit for their mobile banking usage have higher adoption level of mobile banking usage. The financial benefit sensitivity index is the independent X variable in this hypothesis, whereas the behavioral adoption level is dependent on the X variable.
H8: The number of different banks a customer is working with is positively associated with the level of adoption
Split banking, or multiple financial institution usage refers to the alternative terminology created to describe multiple banking behaviors of customers using two or more financial institutions for their financial activities. (Devlin & Gerard, 2005) Moreover, Levesque & McDougall (1996) argues that the variety of banks used by customers has negative
association with the customer satisfaction and retention. As Devlin & Gerard (2005) argues the relationship between customer choice criteria and multiple banking, this hypothesis examines the relationship between multiple banking and mobile banking usage. Therefore, apart from the psychographic drivers to be examined, a computed variable, number of banks a mobile banking user is working with is hypothesized to have a positive effect on mobile banking adoption. Number of banks is the independent/X variable whereas Level of adoption is the dependent/Y variable.
H9: The number of various financial service and products used by a customer is positively associated with the level of adoption.
As multiple bank usage is argued to be related to different financial service/ product usage for different banks by Devlin & Gerard (2005), this hypothesis tests therefore if multiple
service/product users have higher usage of mobile banking due to their higher inter-bank and inter-account activity needs. Number of product/service used is a computed independent variable whilst the adoption level is the dependent variable within this positive X-Y relationship.
5. Research Design
My research will be based on the existing literature, and built upon the questionnaire I will conduct. As I complete my research on the literature, there are several possible drivers that might have different effects on the behavioral adoption level of mobile banking in Turkey. My questionnaire will test the hypotheses that were developed upon theses effect of the drivers and the hypotheses will be possibly proven through my findings. I have chosen survey methodology because I will be able to test my hypotheses on actual people who are Turkish bank customers. Also, this way I’m planning to benefit from my personal
connections from Turkey, which will help me to conduct a better research as well as the improved quality of results, which will have a higher potential for professional implications of my study. The questionnaire has 12 main questions with sub-questions within them. The types of the questions are short answers and/or multiple-choice questions. The total time required to complete the questionnaire is approximately 3 minutes and expected sample size is slightly below 500. The online questionnaire website qualtrics.com will be used for my data collection. I have conducted a pilot survey before I launched my survey in order to test if my questions are clear and direct as well as my questionnaire doesn’t have any technical problems. This includes an offline pilot by asking my supervisor, my colleagues and my friends if the questions are crystal clear and they understand it correctly. Then, I have tested my questionnaire online with a group of approx. 10 people to test the target completion time and technical flawlessness. I’m planning to use Regression analysis to examine my
hypotheses because I’ll test multiple drivers’ effect on a single outcome variable. For the hypotheses testing the moderation effect, simple moderation methodology will be used.
6. Results
In this section, results from the analyses of the data acquired from the survey will be used to test the 9 hypotheses that were discussed in the previous sections. Statistical software Package for Social Sciences (SPSS) version 20 was used to perform statistical analyses. Renaming, recoding, computing scale means, reliability of scales, multivariate regression and simple moderation options were used on the data of 465 participants which was collected in 15 days.
6.1 Preparations
Before starting to analyze data to test my hypotheses, data cleaning was conducted in order to get rid of useless and misleading inputs. Fortunately, 465 respondents are sufficient to be able to have solid sample size after cleaning. Approximately 100 of my respondents left the survey unfinished and there were about 20 surveys with misleading or irrelevant data, such as making jokes on open-ended questions. As Dr. Konus suggested, all the unfinished surveys that haven’t replied to main measure questions and the ones that weren’t filled seriously were deleted. As a result, there were about 350 respondents that were ready to analyze. However, there were still some system missing values due to ‘tick or no tick’ questions and some variables like sex to be changed to use as dummy variables. Therefore, I replaced system-missing boxes with 0s(for tick or no tick questions, for example 0 for not using a banking channel and 1 for using it) and I also recoded the gender variable from 1 for male and 2 for female into 0 for male and 1 for female. I had only two questions that were counter indicative to measure impulsiveness, so I recoded the negatively measured question reversely (1 to 9, 2 to 8 etc..). Finally, respondent IDs were given to each participant and variable names were changed descriptively to make it easier to use later on.
6.2 Sample Descriptives
The final study sample was 341 total participants that consisted of 55.7% males, 39.9% females and 4.4% others. The subjects were aged between 18 and 68 with an average age of 25.6. 233 of the participants (68.3%) were from Istanbul, Turkey and the remaining 31.7% were from other cities in Turkey. 127 of the people who took the survey were high school graduates (37.2%), 173 of them university graduates (50.7%) and 41 (12%) were graduated from a higher degree of education. Before standard multiple regression and simple
moderation analyses were conducted on data, Correlation matrix with the mean and the standard deviation values of the variables were created. None of the independent variables were highly correlated(r<0.9) among each other so no multicollinearity issues were observed. Innovativeness, Number of banks used and number of services used was significantly
correlated but according to Pallant (2005), no significant effect of variables was observed on mobile banking usage(r<0.3). Below Table3 shows the correlations.
*Correlation significant at the 0.05 level (1-‐tailed) ** Correlation significant at the 0.01 level (2-‐tailed)
Variable Mean SD 1 2 3 4 5 6 7 8 9 10 Mobile Banking Usage 7.75 10.26 1 Innovativeness 5.98 2.25 .114* 1 Impulsiveness 5.28 2.32 .036 .034 1 Modernity 7.70 1.39 -‐.038 .145** .047 1 Privacy Concern 8.52 1.45 .014 .093 .086 .405** 1 Time Pressure 6.91 2.19 .061 .201** .154** .161** .172** 1 Financial Benefits 8.29 1.59 .004 .098 .044 .353** .698** .144** 1 Gender 0.49 0.58 -‐.026 .028 .078 -‐.003 -‐.011 -‐.040 .007 1 Number of Banks 2.29 0.63 .125* -‐.004 .021 -‐.103 .016 -‐.089 .024 .134* 1 Number of Services 7.79 1.50 .266** .069 -‐.014 .067 .150** -‐.014 .104 .077 .168** 1
6.3 Reliability
The reliability analysis was conducted on questions that measured same personal trait with different questions so if they have a Cronbach’s alpha value above 0.6 then the scale mean was to be computed into a different variable and used for regression. Among four personality traits that were measured through multiple questions in the survey, only
modernity had a Cronbach’s alpha value above 0.6 (0.660). As a result, only modernity index was computed into a different variable. For the other measures to be used as the independent value, questions that explain a bigger variance during the regression analyses were chosen.
6.4 Hypotheses testing
𝑌 = 𝛼 +β!Χ! +β!Χ!+β!Χ! + 𝜀
Above equation explains the regression model, which will be used for my hypothesis testing. The regression model is used to test the theoretically induced casual relationships between multiple PVs (predictor variable) and single quantitative OV (outcome variable). As we can see from the formula, multiple PVs can be tested simultaneously, relative importance of each PV can be identified and the value of OV can be predicted from values of multiple PVs. Y value in the formula stands for dependent variable (DV)/outcome variable (OV). For our case, number of times a respondent has used mobile banking in the previous month is our Y value. It’s a self-reported number based on the survey entry of the respondent. X is the independent variable (IV)/predictor variable (PV) i of our formula. Every hypothesis has a different independent variable for which the significance of its effect is tested. The relative importance of each PV is measured by its coefficient βi. The ε value is the residual/error term of the model (part of Y not explained by the collection of Xs in the model) whereas α is the constant of the equation. Standard multiple regression model will be used to test H1, H3.H5, H7, H8 and H9. Innovativeness, impulsiveness, modernity, financial benefits, number of
banks used and number of financial services used will be our predictor variables (X variables) respectively. Binary logistic regression model will be used for H2 due to the fact that the dependent variable of H2 is a yes/no question (Initially adopted or not). Independent variable of H2 will be impulsiveness score. For hypotheses 4 and 6 simple moderation model will be used as the hypotheses are testing the moderating effect of sex and time pressure respectively. Impulsiveness and privacy concern will be the independent variables respectively, whereas both hypotheses have behavioral adoption (Number of times m-banking was used previous month) as the outcome variable.
The results of the standard multiple regression analysis is shown in the Table4 below. Table5, Table6 and Table7 in the appendix show the detailed results of the multiple
regression analysis among relevant variables for the specified hypotheses. The R2 of the model was 0.092, which means the model explains 9.2% of the variance in mobile banking usage. The p value of the model is <0.01 (0.000) so the model is statistically significant. Number of products/services used by the customer has the strongest contribution to the model with a beta value of .251 (t=4.711, p<.001) followed by Innovativeness of customer
(beta=.106, t=1.996, p=0.047). Impulsiveness (beta=.038, t=0.725, p=0.469),
modernity(beta= -.059, t= -1.042, p=0.298), financial benefits(beta= -.015, t= -0.261, p=0.794) and number of banks worked with(beta=.077, t=1.441, p=0.151) did not have a significant effect on the model.
Independent Variable B Std.
error Std. β t p Tolerance VIF Innovativeness 0.480 0.241 0.106 1.996 0.047 0.973 1.028 Impulsiveness 0.168 0.231 0.038 0.725 0.469 0.995 1.005 Modernity -‐0.436 0.418 -‐0.059 -‐1.042 0.298 0.848 1.180 Financial benefits -‐0.095 0.362 -‐0.015 -‐0.261 0.794 0.864 1.158 Number of banks 1.243 0.863 0.077 1.441 0.151 0.955 1.047 Number of services 1.711 0.363 0.251 4.711 0.000 0.956 1.046 Table 4: Model table for linear regression tests
For hypotheses 4 and 6, simple moderation tests were conducted. Hypothesis 4 was arguing the moderating effect of sex on impulsiveness and behavioral adoption level relationship. Hypothesis4 could have been supported regardless of the third hypothesis’ result, which was rejecting the impulsiveness ‘effect on behavioral adoption. The model created to examine this hypothesis was explaining 0.61% of the variance of dependent variable. However, the model had a p value of 0.15(>0.05) so the moderation was not significant. This result was enough to reject the hypothesis4. The next simple moderation analysis conducted was for hypothesis6, examining the moderating effect of time pressure on the negative association of privacy concern and behavioral relationship. This time model was explaining 2.95% of the variance and statistically significant p=0.0015(<0.05) however, as we looked at the coefficient box to check the evidence for moderation, the moderating effect of time pressure was not significant. The p-value was 0.27 (>0.05) therefore hypothesis6 was also rejected.
As the final test, binary logistic regression test was conducted to examine the hypothesis2, considering the effect of impulsiveness on initial adoption (downloading the app). The model was statistically significant with a p value of <0.001 (Table8). As we look at the variables in the equation box to check if hypothesis2 was supported or not, we have seen the significant effect of impulsiveness on initial adoption level. (p=0.048<0.05) Moreover, It was also found out that Innovativeness (p=0.022) and Number of products used (p=0.005) have also a significant effect on initial adoption. The Wald values for these variables were w=3.893 for impulsiveness, w=5.210 for innovativeness and w=7.880 for number of products used. The effect of modernity, financial benefits and number of banks used were not
significant. (p>0.05) As a result, the hypothesis2 was supported. Below, Table8 shows the model summary.
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -1.266 .131 93,769 1 .000 .282
Table 8: Model table for binary logistic regression
Considering the results from hypothesis testing overall, below chart states the conclusions on each hypothesis tested.
Hypothesis Result
H1: Innovativeness of a person is positively associated with behavioral adoption level.
𝑆𝑢𝑝𝑝𝑜𝑟𝑡𝑒𝑑
H2: Impulsiveness of a person is positively associated with initial adoption level 𝑆𝑢𝑝𝑝𝑜𝑟𝑡𝑒𝑑
H3: Impulsiveness of a person is positively associated with behavioral adoption level
𝑅𝑒𝑗𝑒𝑐𝑡𝑒𝑑
H4: Positive association of impulsiveness with behavioral mobile banking adoption is stronger for male participants
𝑅𝑒𝑗𝑒𝑐𝑡𝑒𝑑
H5: Modernity of a person is positively associated with behavioral adoption
𝑅𝑒𝑗𝑒𝑐𝑡𝑒𝑑
H6: privacy concerns of the user is negatively associated with behavioral adoption with the moderating effect of time pressure
𝑅𝑒𝑗𝑒𝑐𝑡𝑒𝑑
H7: Customer’s sensitivity on financial benefits is positively associated with behavioral adoption level
𝑅𝑒𝑗𝑒𝑐𝑡𝑒𝑑
H8: The number of different banks a customer is working with is positively associated with the level of adoption
𝑅𝑒𝑗𝑒𝑐𝑡𝑒𝑑
H9: The number of various financial service and products used by a customer is positively associated with the level of adoption
𝑆𝑢𝑝𝑝𝑜𝑟𝑡𝑒𝑑
7. Discussions & Conclusion
Mobile Banking Usage Mobile Banking Installation
Innovativeness √ √
Impulsiveness X √
Modernity X X
Financial Benefits X X
Number of banks used X X
Number of services used √ √
Table 11: Supported Drivers
After the analyses conducted to test the hypotheses proposed, three of the hypotheses were supported. Some of the rejected hypotheses have failed the tests with small margins yet the data provided interesting results. The research has proven the significant effect of being innovative on mobile banking adoption in Turkey as well as the positive association between number of financial product/services used by customers and the adoption level. Moreover, it was among the supported hypotheses that impulsiveness has a positive association with initial adoption of mobile banking.
From the testing results of the first hypothesis, we can conclude that innovativeness is one of the behavioral adoption drivers of mobile banking in Turkey. This means more
innovative people are using mobile banking more often. Since innovativeness is a personality trait, we can also conclude that a customer’s mobile banking usage depends on his/her
characteristics. However, as referred previously, Midgley & Downling (1987) states innovativeness of a person mostly depends on the action to be taken, so this characteristics-behavioral adoption relationship cannot be generalized.
The second hypothesis was arguing the positive effect of impulsiveness on initial adoption and was supported as well. This result was aligned with the expectation, as a person is more impulsive then he would show more spontaneous acts such as downloading a mobile banking application when needed for any financial activity. Impulsiveness is also a personal trait so the results are aligned with Goral et al. (2012) which claims mobile banking usage depends on characteristics of the customer and country specific.
The third hypothesis was also using impulsiveness as the independent variable but this time testing for behavioral adoption, i.e. how often mobile banking is used. This time, results were rejecting the hypothesis claiming impulsiveness of a person has no significant effect on usage frequency of mobile banking. Moreover, the next hypothesis that was testing the
moderating effect of sex in this relationship was also rejected. Although the moderation effect could have been supported despite the rejected direct effect of impulsiveness on behavioral adoption, it was concluded that neither of the effects were significant. On the other hand, it is interesting to see the effect of impulsiveness on initial adoption but not in behavioral
adoption. That means a person’s impulsiveness makes him download the app but not use it, whereas innovativeness has a positive effect on both installation and usage so, it’s more important to be innovative than being impulsive for mobile banking adoption.
The fifth, seventh and eighth hypotheses were rejected. They were testing the positive effect of modernity, financial benefits and number of banks used by customer on behavioral adoption, respectively. The rejection of fifth hypothesis, modernity’s positive effect on behavioral adoption is aligned with Goyal, Pandey & Batra (2012) and conflicting with Greenland & McGoldrick (2012) who says modernity is an pertinent dimension of mobile banking. The rejection of the seventh hypothesis, financial benefits ‘positive effect on behavioral adoption was unexpected and interesting in the sense that many scholars like Laforet & Li (2005) believed that financial benefits m-banking is one of the main drivers of
it. However, it’s highly possible as Laforet & Li (2005) states the importance of customer awareness of financial benefits of mobile banking is crucial for this result. That means, financial benefits sensitivity of a customer does not always lead to m-banking adoption because they might not be aware of the financial benefits of the m banking. This is a promising further research topic for the literature. Apart from the psychographic/attitude-based drivers that were tested so far, the seventh hypothesis was arguing the effect of a behavioral driver, namely the number of banks used. However, it was concluded that number of banks a customer works with, has no significant relationship with mobile banking
adoption. The idea was if a customer works with more banks, he/she would need to increase the inter-account activity which would make it more convenient to use m-banking. Yet, using multiple banks increases the likelihood of having a branch or ATM close by, therefore might decrease the m-banking usage. This also can be a further research.
Hypothesis 6 was testing if time pressure was moderating the negative effect of the privacy concerns of customer on mobile banking adoption. Theoretically, higher privacy concerns were expected to show tendency to less frequent m-banking usage yet, people with high time pressure in their lives were expected to ignore this long term concern for their short term benefit and experience cognitive biased behavior according to cognitive dissonance theory. However, the analysis results showed the unexpected. This moderating effect was not significant. Time pressure in customer’s mind does not moderate their privacy concern’s effect on behavioral adoption.
The final hypothesis tested was the positive relationship between number of financial products/services used by customer and behavioral m-banking adoption. This hypothesis was supported by the analyses. This result is interesting because it claims that people who use more financial services have higher mobile banking adoption level. This can be concluded to suggesting as people in Turkey use more financial services offered by banks, they would
increase their mobile banking adoption level, which will be discussed as an important managerial implication later on in the relevant chapter.
The research has supported three of its hypotheses, stating one psychographic driver for behavioral adoption, one psychographic driver for initial adoption and one behavioral driver for behavioral adoption. The research was based on a questionnaire that provided a rich data with quality and sufficient sample size. One of the reasons for that is having made it obligatory to answer all of the questions to proceed with my questionnaire to avoid missing values as much as possible. Also, as Dr. Konus suggested all the unfinished surveys that haven’t replied to main measure questions were deleted so, we had about 350 respondent data with almost zero modification such as replacement with mean values or neglected empty values.
8. Managerial Implications
The results of this research give important insights about mobile banking adoption in Turkey. There are some remarkable points that organizations that want to benefit from the outcomes should pay attention to. The main implications from the results can be mainly listed as investing on the drivers that are proven to have an effect on adoption and relocating the resources like money, time and effort from the ones that were thought to be effective but rejected based on the analyses.
For instance, the rejected hypothesis of modernity’s effect on mobile banking
adoption emphasizes the market opportunities in smaller and less modern regions. Previously, a commonly made mistake was assuming the mobile banking was in the monopoly of modern regions (Donner & Tellez, 2008) however, these results show that modernity has no
