behavoral intention to adopt internet banking1018195/fulltext02.pdf · bundle and it is an...

MASTER'S THESIS

Behavoral Intention to Adopt InternetBanking

Ikechukwu Okonkwo

Master of Arts (120 credits)Electronic Commerce

Luleå University of TechnologyDepartment of Business Administration, Technology and Social Sciences

To God almighty be the glory

Dedicated to the memory of Samuel Chukwukadibia Okonkwo (Aku kalia) and Mercy Okonkwo

Behavioral intention to adopt internet banking by bank customers May 2012

ii

Abstract The purpose of this thesis is to describe customers’ acceptance of internet banking by identifying factors that explain their intention to use internet banking services in Nigeria. Online banking services have gained increased popularity around the world, however in Nigeria it is a fairly new phenomenon. Online banking service is a part of the e-banking bundle and it is an alternative means of offering the customers expedited self-controlled transactions, using the internet as the medium for the transaction. In today’s reality, banks view internet banking as a powerful ‘value added’ tool to attract and retain new customers.

This study highlights the trends and seeks to identify the factors that influence the customer’s intention towards adopting internet banking services. The overall result will be determined after an in-depth quantitative analysis to know the factors that affects these attitudes.

The information used to write this study was gathered from surveys, books and online data bases.

The result of the study indicates that the customers of knowledge of computers and gender have no influence on intentions to use internet banking. In addition the study was not able to validate some of UTAUT model propositions using the multiple linear regression analysis; however judging from the simple linear analysis most of the UTAUT proposition was validated except the social influence variable which was shown to have no effect on the intention to use internet banking.

Key Words: E-commerce, Technology Acceptance Model (TAM), Diffusion of Innovations, internet, extranet, electronic banking (e-banking), Information and Communication Technology (ICT), Unified Technology Acceptance and Use Theory (UTAUT), Nigeria, Internet banking.


iii

Acknowledgement I wish to express my gratitude to all those who has contributed in one way or the other towards the completion of this study.

First I would like to thank God for life and good health while it all lasted, and then to Lulea University of Technology (LTU) for giving me this opportunity. I want to also show my deep appreciation to Anne Engström, Maria Ek Styvén and Lars-Ole Forsberg, without your individual contributions I may not have been able to see the end of this project. It has been a great honor working and learning from you all.

I want to say a very special thank you to the families of Mr. and Mrs. Stephen Famurewa and Mr. and Mrs. Samuel Awe. I appreciate you’re invaluable contributions towards the success of this project. God bless and reward you all.

On a final note, I would like to thank my family (Mum, Brothers and Sister) and friends too numerous to mention, for all your support. – You know who you are and I say thank you very much. A special mention to Ms. Dinie Cossier, I appreciate your contributions through it all. Ikechukwu Ifediora Okonkwo May 30, 2012.


iv

Contents Abstract .................................................................................................................................................... ii

Acknowledgement ................................................................................................................................... iii

Chapter One - Introduction ..................................................................................................................... 1

1.1 Background .................................................................................................................................... 1

1.2 E-Commerce .................................................................................................................................. 2

1.3 Brief Overview of ICT and Internet Banking in Nigeria .................................................................. 3

1.4 Problem Discussion and Research Purpose ................................................................................... 6

1.5 Thesis Disposition .......................................................................................................................... 7

Chapter Two - Literature Review ............................................................................................................. 8

2.1 Internet banking. ........................................................................................................................... 8

2.2 Benefits of internet banking adoption to bank customers ........................................................... 9

2.3 Barriers to the adoption of internet banking ................................................................................ 9

2.3.1 Trust and Internet Banking Adoption ................................................................................... 11

2.4 Technology Adoption Models and Theories ................................................................................ 12

2.4.1 Innovation Diffusion Theory (IDT) ........................................................................................ 13

2.4.2 Theory of Reasoned Action .................................................................................................. 14

2.4.3 Technology Acceptance Model (TAM) ................................................................................. 16

2.4.4 Unified theory of acceptance and use of technology (UTAUT) ............................................ 17

2.4.5 Conceptualizing Social Psychology Models and Theories .................................................... 19

2.5 Model of trust .............................................................................................................................. 20

2.6 Previous Researches on Customer Adoption of Internet Banking .............................................. 21


v

Chapter Three - Frame of Reference ..................................................................................................... 26

3.1 Unified Technology Acceptance and Use Theory (UTAUT) ......................................................... 27

3.1.1 Operationalization of UTAUT ............................................................................................... 28

3.1.1.1 Operationalization of Performance Expectancy ................................................................ 28

3.1.1.2 Operationalization of Effort Expectancy ........................................................................... 28

3.1.1.3 Operationalization of Social Influence .............................................................................. 29

3.1.1.4 Operationalization of Facilitating Conditions .................................................................... 30

3.2 Model of Trust. ............................................................................................................................ 30

3.2.1 Operationalization of Trust ...................................................................................................... 31

3.3 Behavioral Intentions .................................................................................................................. 32

3.4 Emerged Conceptual Framework ................................................................................................ 32

Chapter Four - Methodology ................................................................................................................. 34

4.1 Research Purpose ........................................................................................................................ 34

4.1.1 Research Approach ............................................................................................................... 34

4.1.2 Research Strategy ................................................................................................................. 36

4.2 Sample Selection ......................................................................................................................... 37

4.2.1 Probability Sampling ............................................................................................................. 37

4.2.2 Non-probability Sampling ..................................................................................................... 38

4.3 Data Collection Method and Analysis ......................................................................................... 38

4.3.1 Primary Data ......................................................................................................................... 39

4.3.2 Questionnaire ....................................................................................................................... 39


vi

4.3.3 Secondary Data ..................................................................................................................... 40

4.4 Literature Search ......................................................................................................................... 40

4.5 Analysis Method .......................................................................................................................... 40

4.6 Method critique ........................................................................................................................... 43

4.7 Reliability and Validity ................................................................................................................. 43

4.7.1 Reliability .............................................................................................................................. 43

4.7.2 Validity .................................................................................................................................. 44

Chapter Five – Data Presentation and Analysis ..................................................................................... 45

5.1 Demographics and Descriptive Statistics .................................................................................... 45

5.1.1 Chi Square Tests ................................................................................................................... 47

5.2 Validation of Measurements ....................................................................................................... 50

5.2. 1 Exploratory factor Analysis .................................................................................................. 50

5.2.2 Reliability .............................................................................................................................. 54

5.3 Test of Model and Hypothesis ..................................................................................................... 55

5.3.1 Multiple Linear Regression Analysis ..................................................................................... 55

5.3.2 Simple Linear Regression ...................................................................................................... 56

Chapter Six - Conclusion ........................................................................................................................ 62

6.1 Academic Contributions of the Study .......................................................................................... 63

6.2 Limitations / Future Research .................................................................................................... 64

References: ............................................................................................................................................ 65

Appendix: A Listed Banks in Nigeria and their e-service channels ....................................................... 78

Appendix: B Survey Instrument ............................................................................................................. 79

Appendix: C Acronyms........................................................................................................................... 80

Appendix: D Cross Correlations Matrix ................................................................................................. 81

APPENDIX: E Histogram Showing Skewness ......................................................................................... 82

List of Figures: ........................................................................................................................................ 83

List of Tables: ......................................................................................................................................... 83


1

Chapter One - Introduction In this chapter the basics and background of the work will be introduced. The topic will be defined and the problem discussion as well as the scope of the study will also be discussed. Ending this chapter will be the disposition of the study.

1.1 Background “Information technology and business are becoming inextricably interwoven. I don't think anybody can talk meaningfully about one without the talking about the other.” - Bill Gates Beginning from the late 1990’s to date internet banking has steadily grown to become a standard offering of most banks. When a new innovation appears it attracts attention and the attention to internet banking is due in part to the rapid diffusion of the internet and growth of e-commerce. Despite this attention very little is known of the systematic information on the scope of Internet banking. Internet banking has been noted to be one channel within the electronic banking (e-banking) bundle (Luštšik, 2003), he defines e-banking as being a combination of the following platforms: (a) Internet banking (or online banking), (b) telephone banking, (c) TV-based banking, (d) mobile phone banking, and (e) PC banking (or offline banking). The emphasis of this work will be on internet banking, which has been noted as one of the most rapidly developing part of e-banking (Ibid). E-banking is also described as including various banking activities that can be conducted through electronic means, from home, business, or on the road instead of at a physical bank location (Turban and King, 2003; Nitsure R.R, 2003). Although e-banking is a bundle of services, the internet has transformed the way e-banking is conducted (Nitsure R.R, 2003). There is no comprehensive data on the number of internet banking users in Nigeria. However the statistics on internet users in Nigeria shows that there has been rapid increases in the number of users (Internet World Stats, 2012), thus it may be logical to assume that users of internet banking services in Nigeria are also increasing. Most banking services are done in the traditional way, but there is an increasing growth of internet banking in Nigeria (Ojeka & Ikpefan, 2011). The reason for investigating internet banking in Nigeria is because report has shown that the internet is one of the preferred channels by customers to transact banking business globally (Capgemini, 2011). Again basic transactions done in the branches are

http://www.brainyquote.com/quotes/quotes/b/billgates173262.html


2

expected to reduce from 37% in 2010 to 34% by 2015 (Ibid), and since this is a fairly new service in Nigeria it therefore makes it attractive to use Nigeria as a case study. Technology acceptance research is a constantly developing field, as new technologies keep evolving all the time (Al-Qeisi, 2009). Over time, information system researchers have developed several theoretical framework that attempt to predict the adoption of technology. The unified acceptance and use of technology (UTAUT) (Venkatesh, Morris,Gordon and Davis, 2003) and theory of trust (Mayer, Davis, Shoorman, 1995) are two of such frameworks for predicting intentions to adopt new innovations. This research will attempt to use the constructs of UTAUT model and Trust to predict intentions to adopt internet banking in Nigeria.

1.2 E-Commerce According to Tassabehji (2003, P.3) there is no all-embracing definition of e-commerce and “there remains a sense of confusion, suspicion and misunderstanding surrounding the area, which has been exacerbated by the different contexts in which e-commerce is used”. Whiteley (2000), states that, just like in other areas of business and information systems, e-commerce is a subject of numerous definitions. Kotler, Wong & Saunders (2005), describe ecommerce as involving the two processes of buying and selling while using electronic means to achieve this, primarily the Internet. E-commerce is also the process of buying, selling, or exchanging products, services, and information via computer networks, including the internet (Turban and King, 2003). Chaffey (2002) on the other hand note that e-commerce goes beyond being an electronically mediated financial transaction between organizations and customers. It also includes non-financial transactions such as customer requests for further information (Ibid). In broader terms, ecommerce is the sharing of business information, maintaining business relationships, and conducting business transactions by means of telecommunications networks (Zwass, 1998). For the purpose of this research, Chaffey’s definition will be adopted because it included both the financial and non-financial parts of e-commerce.

Internet banking is a product of e-commerce in the field of banking and financial services (Rib Docs, 2012).


3

Describing the benefits of e-commerce to banking, Wenninger (2000) states it is important for banks to pursue the conduct of business online, as commerce is shifting towards electronics and if the banks fail to respond to the opportunities posed by the Internet, they could be consigned to a largely secondary role. For the customers internet banking can enable them to save time, take control of their personal finances and even help the environment when they opt to receive electronic statements (Warrington, 2008).

1.3 Brief Overview of ICT and Internet Banking in Nigeria Nigeria is a country of about 170 million people and over 250 tribal groups (CIA, 2012). This reflects in the diversity of cultures and contrasts in the different zones within the country. Internet development and usage have generally increased in Nigeria within the past ten years. In August 2001, licenses for provision of Telecommunication services were auctioned in Nigeria Juwah (2011). The sale of licenses through auction and the following launch of services by the service providers is the turning point for the telecommunication industry in Nigeria. The industry since then has grown in leaps, surpassing all expectations (Ibid). Nigeria’s ICT industry is one of the rapidly emerging sectors of the country and the size of this industry is about 3.2 % of the total GDP of the country, the sector attracts foreign direct investment and private investment as of 2009 to the tune of $18 Billion (NCC, 2012). The telecommunication subscribers have grown from less than 3 million people in the year 2000 to over 80 million people in the year 2010 (NCC, 2012), (see figure 1) with this growth in telecommunication subscribers also came increment in internet users over the same period (internet World Stats, 2012; NCC, 2012).


4

Figure 1: Telecommunications Subscriber data 2000 – 2010

Source: NCC (2012) The latest statistics on world internet users suggests that Nigeria occupies the 10th position amongst the top 20 countries with the highest number of internet users (Internet World Stats, 2012). The study further note a steady rise of internet users in Nigeria from 200,000 in the year 2000 to 43,982,200 (see table 1) in the year 2011


5

Table 1: Top 20 Countries with the Highest Number of Internet Users

Source: Internet world stats (2012) These increments are results of cuts in tariffs which are being influenced by the competitions amongst the service providers (Ndukwe, 2006). The Liberalization policies of the Nigerian Communication Commission (NCC) have made access to Internet services in Nigeria to be less stressful and this facilitates cheaper, faster and better access to Internet services (Ovia, 2001). Again the increasing usage of the internet places Nigeria as a lucrative economy to invest in ecommerce activities (Aghaunor & Fotoh, 2006). The banking industry is one sector within the Nigerian economy where e-commerce is gaining the most grounds. According Ovia (2007), Nigerian consumers are gradually accepting and using e-commerce, and most of the banks now offer internet banking services which allow customers to conduct some form of banking transactions online, from the convenience of their


6

homes or offices. Adeshina & Ayo (2010) however state that even though there are several e-Banking services in Nigeria; there is not enough evidence of consumer acceptance and their stance towards the use of the services. They further note that there is a need to validate customers’ acceptance, attitudes and confidence in the system. According to Muniruddeen, 2007, half of the people who will initially use these e-banking services, usually end up not becoming active users of the system. The typical services offered through internet banking in Nigeria includes; checking/viewing account history, funds transfer within bank and to third party banks, pay bills, confirm and stop cheques, exchange messages with the staff emails and online chart (Zenith Bank, 2012; Diamond Bank, 2012). Appendix 1 shows the table of banks in Nigeria and the e-services they offer.

1.4 Problem Discussion and Research Purpose The main area of interest of this research is on ‘behavioral intention to adopt internet banking in Nigeria.’ As noted earlier the banking industry in Nigeria has shown significant progress in adoption of ecommerce in the delivery of their various services. Several researches have been conducted to analyze internet banking within the Nigerian banking industry (Aghaunor & Fotoh, 2006; Adeshina & Ayo, 2010; Shittu, 2010; Ojeka & Ikpefan, 2011; Oghenerukeybe., 2009). In a previous study, conducted by Adeshina and Ayo (2010) they argued that credibility of the internet banking platform in Nigeria is a major concern for both users and intending users and should be given more attention. Also studies show that there is low level of trust in the security measure of e-banking technology and the ability of e-Banking systems in Nigeria to protect privacy (Adeshina & Ayo, 2010). Noting further on trust, Avinandan and Prithwiraj (2003) while quoting Aladwani (2001), stated that consumers trust in online banking is a critical challenge facing bank managers, and this warrants a further study on the subject. According to Efendioglu, Yip, Murray (2004), most consumers in developing countries do not support e-commerce, and this is a result of lack of confidence in technology and online culture. This therefore behooves the banks to strive to understand the reasons or factors that influence internet banking adoption by their customers and potential customers. Dong, Liu, Qian, Fang. (2008) states that although banking institutions have spent millions of dollars in building online banking systems, reports have shown that potential users are not using the


7

systems as expected for various reasons. Thus, there is a need to identify the factors that determine customer acceptance of Internet banking services (ibid.). In the Nigerian context, there is a need to study e-commerce adoption from the banking customers’ perspective (Aghaunor & Fotoh, 2006) and investigation has shown that there is not enough evidence of consumers´ acceptance of e-banking services in Nigeria (Adeshina & Ayo, 2010), therefore more research needs to be conducted in this area of study to further verify the intentions of the customers towards acceptance of internet banking. Several technology acceptance models have been used to investigate the behavior of users in relation to their intentions to adopt. Interestingly very few of these researchers have used the UTAUT model, even though it has been noted to be a parsimonious and robust model in investigating user intentions (Al-Qeisi, 2009). There is the problem of trust also, which is shown to have a strong positive influence on behavioral intention to adopt a new technology, and it is not included in the original UTAUT model (Lee, Kim & Song, 2010). The purpose of this research therefore, will be to describe customers’ acceptance of internet banking by identifying factors that explain their intention to use internet banking services. This will be done using the elements of the UTAUT model and the Trust model.

1.5 Thesis Disposition This disposition shows briefly how the thesis is written and organized (See figure 2). The thesis is organized in 6 chapters following this order. Figure 2: Thesis Disposition Source: Authors Diagram


8

Chapter Two - Literature Review In the previous chapter, the background, problem discussion and the specific research problem were presented. In this chapter, an overview of related literature on adoption of Internet banking will be provided, while aiming at building a theoretical framework for the research problem

2.1 Internet banking. Customers through internet banking can perform a wide range of banking transactions electronically via the bank’s website and, this means that for a customer, internet banking provides a convenient channel to manage ones financial transaction (Tan & Teo, 2000). Through internet banking, sophisticated cash management packages can be used to access up to the minute information, which will enhance decision making as regards to fund management (Ibid). The increasing customer demands, increasing competition amongst the banks, worldwide deregulation of financial services market and the drive by banks to be more efficient and reduce cost is some of the factors that are driving the acceleration of internet banking (Hutchinson and Warren, 2003 while quoting National Office for the Information Economy, 1999). Ovia (2001) describe internet banking services in three ways:

Informational – this is when a bank’s internet services are simply to display their products and services their websites. This type of service do not allow for any form of transaction on the banks website.

1. Communicative – the bank’s system allows interaction between the system and the customer. This interaction is limited to electronic, account opening enquiry, loan application and static file updates. Because these servers have a path to the bank’s internal network, the risk is higher compared to informational.

2. Transactional – this level of Internet banking allows banks to transact business with their customers. It presents the highest risk architecture and must have the highest security and controls.


9

2.2 Benefits of internet banking adoption to bank customers Aladwani (2001) note that providing faster, easier and more reliable services to customers are amongst the top drivers of e-banking development. The spread of internet e-commerce will depend on the perception of the consumer of its advantages and disadvantages (Ibid). Among the benefits of e-commerce to bank customers are:

1. Electronic banking saves time and money for users and consumers can use e-banking to pay bills online or to secure a loan electronically (Turban and King, 2003).

2. Online banking is most appropriate for most simple enquiries and money transfer and

internet banking have reduced the need to visit the branch (Whiteley, 2000).

3. Ovia (2001) note that through internet banking, customers would enjoy sitting in the comfort of their homes and offices and with a PC log onto their banks’ servers and transact banking activities.

4. There are no geographical or national boundaries in electronic commerce. It is only

limited by the coverage of computer networks. Customers can select from all potential suppliers regardless of their whereabouts (Ecommerce info center, 2012).

5. Online real-time nature of the medium means that with e-banking customers can view

real time balances and transactions on their account, pay bills etc. (Zenith Bank, 2012).

2.3 Barriers to the adoption of internet banking OECD (1998) note that e-commerce provides users with considerable benefits, in the form of increased choice, access to goods and services, and a new medium for interaction with users and suppliers. They further said that, businesses and consumers will not embrace e-commerce until they have confidence that their use of services on open networks are secure and reliable; that their transactions will be safe and private; that they will be able to prove the origin, receipt and integrity of the information they receive; that they can identify those they are dealing with; and that there are appropriate mechanisms available to them if something goes wrong.


10

A summary of some of the different barriers to internet banking by consumers based on extensive review of literatures is presented in Table 2. It is interesting to note that one of the most cited barrier to adoption of ecommerce by both businesses and consumers is ‘Trust’, this barrier arises from the fact that businesses fear that hackers will break into their networks and steal valuable information (Lawrence et al, 1998). Also the privacy of personal details and security of financial transactions are a concern to many users and potential users of e-commerce (Whiteley, 2000). Some researchers have categorized the barriers into four groups namely, technology infrastructure, private sector, framework of laws, and digital consumers’ behavior (Xanthidis and Nicholas, 2004). While Turban and King, (2003) grouped the barriers under two broad headings technological and non-technological limitations. MacGregor and Vrazalic (2005), note that although previous researches have attempted to categorize ecommerce adoption barriers in order to make sense of the diverse range of barriers identified in empirical studies, these attempts have not resulted in a model of e-commerce barriers that would explain how they are related. Table 2 Summary of barriers to internet banking adoption by consumers Barriers to Internet banking adoption

Most Affected Related Literature

Concerns about Security and privacy of information: credit cards, financial and personal data, Hackers, Viruses.

Private Customers / Corporate Customers

Timmers (1999), OECD (1998), Turbman and King (2003), Lawrence et al. (1998)

Compatibility issues of different technologies from different vendors: due to competition

Corporate customers Turbman and King (2003), Timmers (1999)

Cultural barriers: some customers like to feel and touch products: resistant to change.

Private Customers Whiteley (2000), Turbman and King (2003)

Legal frame work issues: uncertainty about applicable laws and conflicting international laws


OECD (1998), Lawrence et al. (1998), Xanthidis and Nicholas, 2004

Matter of trust: consumers have trust issues concerning someone they cannot see but should trust.

Private Customers Turbman and King (2003) OECD (1998).

Lack of IT infrastructures: Corporate customers Xanthidis and Nicholas


11

telecommunication bandwidth is insufficient.

(2004), Turbman and King (2003), Lawrence et al. (1998), Ovia (2001).

Lack of usability of the technology: Low awareness and knowledge of how the system works. Lack of education.

Private Customers Timmers (1999), Khatibi et al. (2003)

Cost issues: cost of deploying the relevant platforms and cost of accessibility by the consumers.


OECD (1998),Turbman and King (2003), Iacovou et al. (1995), Jones et al (2004)

Lack of personal service: e-commerce significantly reduces or in some cases puts an end to human to human contact.

Private Customers Kangis, P & Rankin, K (1996), Lawrence and Tar (2010).

Lack of skilled workers to handle/ maintain E-commerce systems: the inadequacy of trained ecommerce managers affects the adoption level.

Corporate Customers Khatibi et al. (2003)

Technology readiness of corporate customers plays a role in their attitudes towards technology: this is their positive / negative feeling towards technology.

Corporate Customers Rotchanakitumnuai and Speece, (2003).

Source: Author Diagram

2.3.1 Trust and Internet Banking Adoption Trust is the cornerstone in e-commerce. Traditionally, approaches to trust rely on physical contact and paper based business process (Ren & Hassan, 2008). The question of trust may be even more important in the virtual world than it is in the real world, since the two parties are not in the same place, and, hence, cannot depend on things like physical proximity, hand-shakes and body-signals to make up opinions (Clark, 1995). The physical distance to Internet merchants, the absence of sales people, and the separation between consumers and products on the Internet, increases the lack of trust within e-commerce activities (An & Kim, 2008). Human trust is a subjective, context-dependent expectation that the trustee will choose a specific action in the encounter (Wierzbicki, 2008).


12

Trust has been variously defined by different social psychology researchers. Wierzbicki, (2010) defines trust as the relation between a trustor and a trustee in a context. For Michalos (1990), trust is a relatively informed attitude or propensity to allow one and perhaps others to be vulnerable to harm in the interest of some perceived greater good. Hosmer (1995), while looking at trust from an economic point of view, defines it as an optimistic expectation of the behavior of a stakeholder of the firm under conditions of organizational vulnerability and dependence. Efendioglu (2005) states in his paper that in e-commerce transactions, trust extends beyond the buyer and seller to institutions including online payment firms, banks, credit card companies and the Internet provider. Internet banking transaction is a trusting behavior; this is because the customer makes him or herself vulnerable by putting trusts on the internet to complete his or her transaction (Mayer, Davis & Shoorman, 1995). According to Dong et al. (2008), all interactions require an element of trust, especially those conducted in the uncertain environment of e-commerce. Internet banking is highly uncertain as the parties involved in the transactions are not in the same place (Clark, 1997). Furthermore, Mukherjee and Nath (2003) state that customers believe that the internet payment channels are not secure and can actually be intercepted, which reduces the customers’ level of trust, discouraging them from engaging in online information search and making online banking transactions. In summation, Lee et al. (2010) states that previous ICT adoption literatures shows that trust and perceived risk are critical factors to be considered in order to explain users’ acceptance of ICT in the e-Business environment. In addition, customer trust is also considered to be a major factor influencing internet banking (Baraghani, 2007).

2.4 Technology Adoption Models and Theories Internet banking is a fairly new innovation (Laforet & Li, 2003) and an innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption (Rogers, 2003). The perceived newness of an idea for an individual determines his or her reaction to it (Ibid). Technology is inherently difficult to manage because it is constantly changing, often in ways that cannot be predicted (Reference for business, 2012).


13

Several theories and models have been developed to explain information systems from a social psychology standpoint. Rogers (1962) developed the Diffusion of Innovations (DOI) or Innovations Diffusion Theory (IDT) to explain why and how people adopt new technologies and the rate at which they do so. Fishbein and Ajzen (1975) propose the Theory of Reasoned Action (TRA), which has been variously validated and proven to be successful in explaining adoption behaviors of consumers towards various technology systems. Davis´ (1989) Technology Acceptance Model (TAM) is an information system model that shows how users accept and use technology. This model was further developed by Venkatesh and Davis (2000) into the Unified Theory of Acceptance and Use of Technology (UTAUT).

2.4.1 Innovation Diffusion Theory (IDT) Innovation Diffusion Theory (IDT) is a model that shows how and why users adopt an innovation and at what rate they adopt these innovations (Rogers, 2003). The rate of adoption is the relative speed with which members of a social system can adopt an innovation or technology and it is measured by the length of time required for a certain percentage of the members of a social system to adopt an innovation (Ibid). It was developed and introduced by Rogers in 1965 (Rogers, 2003). Diffusion is the process in which an innovation is communicated through certain channels, over time, among the members of a social system (Rogers, 2003). There are four main elements in the IDT: (1) an innovation (2) communication channels (3) time and (4) social system.

IDT posits that certain attributes of innovation, as perceived by individuals, help to explain their different rates of adoption. Rogers (2003, p 229-258), define these attributes as:

1. Relative advantage – “the degree to which an innovation is perceived as better than the idea it supersedes. The degree of relative advantage may be measured in economic terms, but social prestige factors, convenience, and satisfaction are also important factors.”

2. Compatibility – “the degree to which an innovation is perceived as being consistent

with the existing values, past experiences and needs of potential adopters. An idea that is incompatible with the values and norms of a social system will not be adopted as rapidly as an innovation that is compatible.”


14

3. Complexity – “the degree to which an innovation is perceived as difficult to

understand and use”.

4. Trialability – “the degree to which an innovation may be experimented with on a limited basis. New ideas can be tried on the installment plan will generally be adopted more quickly than innovations that are not divisible.”

5. Observability – “the degree to which the results of an innovation are visible to others.

The easier it is for individuals to see the results of an innovation, the more likely they are to adopt.”

Several authors’ note that relative advantage, compatibility and complexity are the three most used attributes in relation to adoption of innovation (Baraghani, 2007).

2.4.2 Theory of Reasoned Action The Theory of Reasoned Action (TRA) was developed by Martin Fishbein and Icek Ajzen (1975, 1980). The theory is based on assumptions that human beings are usually quite rational and make systematic use of the information available to them (Ajzen & Fishbein, 1980). Further, TRA is concerned with the determinants of intended behaviors and a person’s intentions are a function of two basic determinants, one is personal in nature and the other reflecting social influence. Attitude towards the belief (ATB) is a personal factor which influences an individual’s positive or negative evaluation of performing a behavior. Subjective norms (SN) are a person’s perception of social pressures to perform or not to perform a behavior. TRA is made up by three general constructs: behavioral intention (BI), attitude (A), and subjective norm (SN). (Ibid) Theory of Reasoned Action posits that a person's behavioral intention depends on the person's attitude towards the behavior and subjective norms (BI = A + SN) (Ajzen & Fishbein, 1980). Davis et al., (1989) notes that a person's performance of a specified behavior is determined by his or her behavioral intention (BI) to perform the behavior, and BI is jointly determined by the person's attitude (A) and subjective norm (SN) concerning the behavior in question.

According to Baraghani (2007), behavioral intention is expected to predict actual behavior accurately if the following three boundary conditions, specified by the theory, can be held: (1) the degree to which the measure of intention and the behavioral criterion correspond with


15

respect to their levels of specificity of action, target, context and time frame. (2) The stability of intentions between time of measurement and performance of the behavior and (3) the degree to which carrying out the intention is under the volitional control of the individual (i.e., the individual can decide at will to perform or not to perform the behavior). Sheppard et al. (1988), note that the Theory of Reasoned Action has received very considerable and also justifiable attention within the field of consumer behavior. The model appears to predict consumer intentions and behavior very efficiently (ibid). It also provides a relatively simple basis for identifying where and how to target consumers' behavioral change attempts (ibid). The major application of the theory of reasoned action is in the prediction of behavioral intention, including predictions of attitude and predictions of behavior. The subsequent separation of behavioral intention from behavior allows for explanation of limiting factors on attitudinal influence (Ajzen & Fishbein, 1980). Figure 3: Theory of Reasoned Action Source: Ajzen and Fishbein (1980, P.8)

The person’s beliefs that the behavior leads to certain outcomes and his evaluations of these outcomes

The person’s beliefs that specific individuals or groups think he should or should not perform the behavior and his motivation to comply with the specific referents

Attitude toward the behavior

Relative importance of aatitudinal and normative

considerations

Subjective Norm

Intention Behavior

http://en.wikipedia.org/wiki/Behavioral_intention

http://en.wikipedia.org/wiki/Behavioral_intention

http://en.wikipedia.org/wiki/Attitude_(psychology)

http://en.wikipedia.org/wiki/Attitude_change


16

2.4.3 Technology Acceptance Model (TAM) Technology Acceptance Model (TAM) is a widely used model, introduced by Davis (1989). It is an extension of the Theory of Reasoned Action (TRA). TAM specifically models the acceptance of information systems. The aim of TAM is to provide answers to how users come to accept technology. The model suggests that a number of factors influence user’s decision on how and when to accept a new technology. The model pursues a better measurement for predicting and explaining use of technology, it notes that perceived usefulness and perceived ease of use are the two main factors that influence this decision. (Ibid)

According to Davis (1989), perceived usefulness is the degree to which a person believes that using a particular system would enhance his or her job performance. Furthermore, he defines perceived ease of use as the degree to which a person believes that using a particular system would be free from effort. Behavioral intention (BI), as represented in TAM, is seen as being jointly determined by a person's attitude toward using a system (A) and perceived usefulness (U), with relative weights estimated by regression: BI= A + U.

The Technology Acceptance Model is indeed a very popular model for explaining and predicting system use (Chuttur, 2003). Several social psychology researchers have replicated Davis (1989), original model, to show the relationship between ease of use of a system, perceived usefulness of a system and the actual system use. Venkatesh and Davis (2000) introduced a unified model by introducing social influence as a factor that can influence the perceptions of the user. Figure 4: Technology Acceptance Model Source: Davis (1989, P. 985)


17

2.4.4 Unified theory of acceptance and use of technology (UTAUT) The Unified theory of acceptance and use of technology (UTAUT) model is an extension of the Technology acceptance model formulated by Venkatesh, Morris, Gordon & Davis (2003). It is a review of 8 other theories, previously used in social psychology analysis. According to Venkatesh et al., (2003), UTAUT provides a useful tool for managers needing to assess the likelihood of success for new technology introductions. UTAUT is also used to understand the drivers of acceptance in order to proactively design interventions targeted at populations of users that may be less inclined to adopt and use new systems (Ibid). The UTAUT model has advanced individual acceptance research by unifying the theoretical perspectives common in the literature and incorporating four moderators to account for dynamic influences including organizational context, user experience, and demographic characteristics (Venkatesh et al., 2003). Oshlyansky Cairns & Thimbleby (2007), state that: “UTAUT tool may be useful in providing insight into cross-cultural technology acceptance differences.” According to Park Jung, Kun, SuJin Yang, Park (2007), while reviewing the UTAUT model they note that UTAUT has been considered the most prominent and unified model in the stream of information technology adoption research with high robustness of the instruments regarding the key constructs. Dulle & Minishi-Majanja (2011) agree that UTAUT is comprehensive and has high explanatory power as compared to other technology acceptance and use theories. As shown in Figure 5, the theory provides an explanation to user intentions to use a technology and the subsequent usage behavior. The theory also holds that the four key constructs, performance expectancy; effort expectancy; social influence; and facilitating conditions, are direct determinants of usage intention and behavior. Gender, age, experience, and voluntariness of use are to mediate the impact of the four key constructs on usage intention and behavior. (Venkatesh et al., 2003)


18

Figure 5: Unified theory of acceptance and use of technology (UTAUT)

Source: Venkatesh et al. (2003: P.447) In explaining the four core constructs of UTAUT, Venkatesh et al (2003), note that performance expectancy could be defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance. This construct was, according to them, derived from five different constructs and models of perceived usefulness (TAM/TAM2 and C-TAM-TPB), extrinsic motivation (MM), job-fit (MPCU), relative advantage (IDT), and outcome expectations (SCT). Furthermore, Venkatesh et al., (2003) define effort expectancy as the degree of ease associated with the use of the system. This concept was derived from the three constructs and models of perceived ease of use (TAM/TAM2), complexity (MPCU), and ease of use (IDT). The third construct, used in explaining UTAUT, is the social influence. Venkatesh et al., (2003) define this construct as the degree to which an Individual perceives that important others believe he or she should use the new system. Social influence in UTAUT is explained as subjective norm in TRA, TAM2, TPB/DTPB and C-TAM-TPB, social factors in MPCU, and image in IDT. Finally the last construct in UTAUT is the facilitating conditions, defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system. Facilitating conditions is represented as


19

perceived behavioral control (TPB/ DTPB, C-TAM-TPB), facilitating conditions (MPCU), and compatibility (IDT). (Ibid) An attempt by Lee et al., (2010) to extend the UTAUT model indicates that Trust and perceived risk have effect on intention to use in UTAUT.

2.4.5 Conceptualizing Social Psychology Models and Theories The table below presents a conceptualization of all the different concepts and theories mentioned earlier as conceptualized by Venkatesh et al., (2003). Table 3: Models and Theories of Individual Acceptance Innovation Diffusion Theory (IDT) Core Constructs Grounded in sociology, IDT has been used since 1960’s to study a variety on innovations, ranging from agricultural tools to organizational innovations.

1 Relative Advantage 2 Ease of Use 3 Image 4 Visibility 5 Compatibility 6 Result Demonstrability 7 Voluntariness of Use

Theory of Reasoned Action (TRA) Core Constructs Drawn from social psychology. TRA is one of the most fundamental and influential theories of human behavior. It has been used to predict a wide range of behaviors.

1 Attitude Toward Behavior 2 Subjective Norm

Technology Acceptance Model (TAM) Core Constructs TAM is tailored to IS contexts, and was designed to predict information technology acceptance and usage on the job. Unlike TRA, the final conceptualization of TAM excludes the attitude construct in order to better explain intention parsimoniously.

1 Perceived Usefulness 2 Perceived Ease of Use 3 Subjective Norm

Unified Technology Acceptance and Use theory (UTAUT) Core Constructs UTAUT extended TAM by objectively studying 8 other social psychology and technology adoption models.

1 Performance Expectancy 2 Effort Expectancy 3 Social influence 4 Facilitating conditions

Source: Venkatesh et al., (2003: P.427-432)


20

2.5 Model of trust Mayer et al. (1995) define trust as the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party. One factor that will affect the trust that one party has for another involves traits of the trustor, referred to as the “propensity to trust”. To build trust, the trustee must first be trustworthy. (Ibid) Even though a number of factors have been proposed, by other researchers within social psychology, ability, benevolence, and integrity has been noted as the three characteristics of a trustee that influences their trustworthiness (Mayer et al., 1995). While noting the importance of trust as a key facilitator of e-commerce, Bhattacherjee (2002), propose a scale that uses the three key dimensions of trust i.e. trustee's ability, benevolence, and integrity. Ability is described as group of skills, competencies, and characteristics that enable a party to have influence within some specific domain (Mayer et al., 1995). They also state that the domain of the ability is specific because the trustee may be highly competent in some technical area, affording that person trust on tasks related to that area.” Bhattacherjee (2002) refers to ability as the perception of the consumer about the competency and salient knowledge of the mobile banking service provider to deliver the expected service. Benevolence is described as the extent to which a trustee is believed to want to do good to the trustor, aside from an egocentric profit motive (Mayer et al., 1995). Benevolence suggests that the trustee has some specific attachment to the trustor (Ibid). Bhattacherjee (2002) describe it as the extent to which a service provider will demonstrate receptivity and empathy towards the user. Further, the service provider will make a good faith effort to resolve users’ concerns and intends to do good to the users beyond profit motives (ibid.). Finally, Mayer et al., (1995) explained that integrity is the trustor's perception that the trustee adheres to a set of principles that the trustor finds acceptable. Integrity is users’ perceptions that the service provider will be fair, honest and adhere to reasonable conditions of transactions (Bhattacherjee, 2002). The propensity to trust is a stable within-party factor that will affect the likelihood of one party to trust another (Mayer et al., 1995). Propensity will influence how much trust one has for a trustee prior to data on that particular party being available. People with different


21

developmental experiences, personality types, and cultural backgrounds vary in their propensity to trust. (Ibid)

Figure 6: Model of trust

Source: Mayer et al. (1995, p715)

2.6 Previous Researches on Customer Adoption of Internet Banking Adesina and Ayo (2010) used the Technology Adoption Model (TAM) as the basis for the analysis, in their study of users’ acceptance of e-Banking in Nigeria. The study focused on examining the factors that influence users’ acceptance of e-Banking taking to consideration their attitude and confidence in the use of the system. The study examined perceived usefulness (PU), perceived ease of use (PEOU), perceived credibility (PC), computer self-efficacy (CSE), on customer attitude as factors that can determine the level of users’ acceptance of the various e-Banking services. Results from the study show that perceived usefulness is the critical factor in explaining users’ adoption of e-banking. In addition, it was found that credibility of the system is a major concern for both users and intending users and should be given more attention and there is low level of trust in the security measure of e-banking technology and the ability of e-Banking systems to protect privacy (Adesina & Ayo, 2010).


22

Chaipoopirutana, Combs, Chatchawanwan (2009) used Rogers´s (1995) Innovation Diffusion Theory (IDT) to empirically test parts of the attributes of IDT and the adoption of internet banking in Thailand and India. They noted that there are five key attributes to IDT (Relative Advantage, Compatibility, Complexity, trialability, Observability), but for the purpose of their research, observability was excluded.They also stated that customer adoption level of Internet banking is not very high for most banks in India and Thailand.. A summary of their findings indicate that whereas relative advantage, compatibility, and trialability significantly positive effect on customer’s attitude toward Internet banking, the results for complexity indicated that complexity has a negative effect on intentions to adopt internet banking innovation..In their final conclusion they advised that banks should concentrate more in marketing to their existing customers and less in trying to attract new users on internet banking. Dong, Liu, Qian & Song (2008) examined customer acceptance of internet banking. This research employed the Unified Technology Acceptance and Use theory (UTAUT) to study the possible factors that could affect a customer's intention to adoption internet banking. In addition, they investigated the cause-and-effect relationships among these factors Deng et al. (2008), argue that user satisfaction is typically viewed as the attitude that an individual has toward an information system (IS) and this attitude in turn influences his or her adoption intention. Their findings include that Bank managers should cultivate and solidify a positive perception of how useful Internet banking is to the intended users. They also noted that it is important for Internet banking to make users feel secure. The importance of giving attention to the design of an Internet banking site by Managers was also underscored in this research. In Amin’s (2008) research on internet banking adoption among young intellectuals, he tried to explain the factors influencing undergraduate students´ acceptance of internet banking in Malaysia and the theoretical frame work of the study is based on the technology adoption model (TAM). Amin (2008) found that Malaysians are still reluctant to adopt internet banking because of several reasons, including security and privacy issues, although the system provides flexibility in performing financial transaction, fast and easy, Based on his results, Amin (2008) proposes that investments to improve internet banking should be a continuous process by the banks and continuous indoor training of student customers and security assurances should be treated as priority.


23

Baraghani (2007) studied the factors influencing the adoption of internet banking in Iran, as an example of a developing country. The study applied the UTAUT model to determine the current state of consumer attitude and beliefs towards internet banking. Baraghani (2007) in her study also tried to propose opportunities for both participants and researchers to uncover unseen problems, thereby improving the use and acceptance of internet banking. The results of the study recognized that both technological and trust-based issues are very important in increasing customers´ behavioral intentions to use internet banking. Baraghani (2007).also noted that social influences are very important in influencing non users. Similar to Dong et al. (2008), Pikkarainen, Pikkarainen, Karjaluoto, & Pahnila (2004) studied the consumer acceptance of online banking. The research focuses on online banking acceptance from the information systems acceptance point of view, while referring to the idea that consumers are using banks information system. Online banking, in their study, is defined as an Internet portal, through which customers can use different kinds of banking services, ranging from bill payment to making investments. Therefore banks’ Web sites that offer only information on their pages, without possibility to do any transactions, are not qualified as online banking services. The result of their study shows that perceived usefulness and information on online banking on the website are the main factors influencing online banking acceptance (Pikkarainen et al., 2004). Yi-Shun, Wang, Lin, Tang (2003) studied the determinants of user acceptance of Internet banking. The objective of their research was to extend the TAM in the context of Internet banking. They propose a new construct, “perceived credibility”, to enhance the understanding of an individual’s acceptance behavior of Internet banking. Their research also identifies critical individual different variables (i.e. computer self-efficacy) that have a significant effect on the intention, of potential users, to use Internet banking. The result of their research was consistent with the hypothesis, that users who have a higher computer self-efficacy are likely to have a more positive usefulness and ease of use beliefs but have more negative credibility belief about the internet banking A compilation of previous research on internet banking is presented in table 4 below:


24

Table 4: Summary of Previous Research on Internet Banking Author Research Title Model Used Factors

Adesina and Ayo (2010)

An Empirical Investigation of the level of users’ acceptance of e-Banking in Nigeria

TAM

• Perceived Usefulness • Perceived ease of use • Perceived credibility • Computer self-efficacy

Chaipoopirutana, Combs, Chatchawanwan (2009)

Adoption of internet banking in Thailand and India

IDT

• Relative Advantage • Compatibility • Ease of Use • Trialability

Dong Cheng, Gang Liu, Cheng Qian, Yuan-Fang Song (2008)

Customer Acceptance of Internet Banking: Integrating Trust and Quality with UTAUT Model

UTAUT and Trust

perception, Quality

Attributes

• Behavioral Intention • Performance Expectancy • Trust Perception • Quality Attributes

Amin (2007) Internet banking adoption among young intellectuals

TAM

• Perceived Usefulness • Perceived ease of use • Perceived credibility • Computer self-efficacy

Baraghani (2007) Factors influencing the

adoption of internet banking

TAM and

TPB

• Perceived Usefulness • Perceived ease of use • Trust • Perceived behavioral

control • Subjective norms

Pikkarainen et al. (2004)

Consumer acceptance of online banking: an extension of the technology acceptance model

TAM and

Focus Group

• Perceived Usefulness • Perceived Ease of Use • Perceived Enjoyment • Information of Online

Banking


25

• Security and privacy • Quality of internet

connection

Yi-Shun et al. (2003)

Determinants of user acceptance of Internet banking

TAM

• Perceived credibility • Perceived usefulness • Ease of use • Computer self-efficacy • Intention

Tan M. and Teo T. (2000)

Factors Influencing Adoption of Internet banking

TPB and DOI

• Attitude • Subjective Norm • Perceived Behavioral

Control Source: Compiled by the Author


26

Chapter Three - Frame of Reference The previous chapter discussed the relevant literatures needed and used for this research. In this chapter the relevant literature from chapter two will be used to design a conceptual frame of reference that will show how the research questions will be answered as well as support the arguments that will be put forward in the research.

According to Zaltman & Barabba (1991), a frame of reference is the viewing lenses for describing, explaining and controlling events. Frame of reference contains the truth test managers and researchers use when evaluating the accuracy of data, their interpretation and application.

In addition, the frame of reference explains the key factors, constructs or variables and their presumed relationships either graphically or in a narrative form (Miles & Huberman, 1994). In this study, both narrative and graphical representation will be used to present the frame of reference.

The purpose of this research is to describe customers’ acceptance of internet banking by identifying factors that explain their intention to use internet banking services. According to Park et al (2007), UTAUT has been validated in empirical settings as having superior explanation power over past models. “UTAUT has been considered the most prominent and unified model in the stream of information technology adoption research with high robustness of the instruments regarding the key constructs” (Park et al, 2007 while quoting Li and Kishore, 2006).

The four key variables of UTAUT model which are determinants of usage intention will be used to describe the factors that influence end-use consumers´ intention. This is because the thesis focuses only on the direct determinants of intentions. Therefore the measurement of actual use or adoption behavior and the mediating factors (Venkatesh et al., 2003) of the determinants are not included in this research. The variables from the model of trust (Mayer et al., 1995) will also be included to describe the influence of trust on intentions.

Based on the problem statement and the purpose of conducting the research, the following set of hypotheses are proposed to be tested:


27

Hypothesis 1: Performance Expectancy (PE) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria. Hypothesis 2: Effort Expectancy (EE) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria. Hypothesis 3: Social influence (SI) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria. Hypothesis 4: Facilitating Conditions (FC) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria. Hypothesis 5: Trust (TR) has a positive effect on the customer’s behavioral intentions to adopt Internet banking in Nigeria. Hypotheses 1 - 4 were proposed based on the Unified Technology Acceptance and Use Theory. Hypothesis 5 is the unique features from Trust, which have been expressed by different social psychology researchers as being a barrier to technology adoption (Lee et al., 2010). The choice of Trust is predicated on the study of Lee et al. (2010), they noted that trust has been omitted in the original UTAUT model and that trust has been shown to have a direct positive impact on Behavioral Intention (BI).

3.1 Unified Technology Acceptance and Use Theory (UTAUT) The theory provides an explanation to user intentions to use a technology, and the subsequent usage behavior. The theory also holds that four key constructs (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) are direct determinants of usage intention and behavior (Venkatesh et al., 2003). This model will be adopted in the design of the conceptual framework and the analysis of the results thereof. The choice of the UTAUT model for this study was motivated by its comprehensiveness and high explanatory power as compared to other technology acceptance and use theories (Dulle & Minishi-Majanja, 2011). The measurements suggested by Davis et al. (2003) will be adapted, for the operationalization of UTAUT constructs, to reflect the specific target behavior - intention to use internet banking.


28

3.1.1 Operationalization of UTAUT

3.1.1.1 Operationalization of Performance Expectancy Table 5: Operationalization of Performance Expectancy

Performance Expectancy: (Hypothesis-1) Code – PE Construct Operational Definition Measurement*) Adapted from Relative Advantage

The degree to which using an innovation is perceived as being better than using its precursor

1.Using I-banking would enable me to accomplish banking tasks more quickly 2. Using I-banking will increase the quality of my banking services activities 3. Using internet banking will improve the effective use of my time in handling my banking tasks 4. I find using I-banking more useful than visiting the physical bank

Venkatesh et al. (2003)

*Measurement of this variable is on a five point Likert scale, indicating strength of agreement, ranging from (1) “strongly disagree” to (5) “strongly agree”. Previous work on UTAUT and related literature suggests that the Construct relative advantage has an impact on intention to use. This led to proposing hypothesis 1: H1: Performance Expectancy (PE) has a positive influence in the customers’ behavioral intentions to adopt Internet banking in Nigeria.

3.1.1.2 Operationalization of Effort Expectancy Table: 6 Operationalization of Effort Expectancy

Effort Expectancy: (Hypothesis – 2) Code EE Construct Operational Definition Measurement*) Adapted from Perceived Ease of Use

The degree to which a person believes that using a system would be free of effort.

1. Learning to use the Internet banking system is easy for Me 2. I would find it easy to get the Internet banking system to do what I want it to do 3. My interaction with Internet banking is clear and understandable 4. I am skillful at using Internet banking system


*Measurement of this variable is on a five point Likert scale, indicating strength of agreement, ranging from (1) “strongly disagree” to (5) “strongly agree”.


29

Based on the UTAUT model, which suggest that effort expectancy (EE) has a strong impact on user’s intention to adopt technology services, the following hypothesis to test this in a different market is proposed: H2: Effort Expectancy (EE) has a positive influence in the customers’ behavioral intentions to adopt Internet banking in Nigeria.

3.1.1.3 Operationalization of Social Influence Table: 7 Operationalization of Social Influence

Social Influence: (Hypothesis – 3) Code – SI Construct Operational Definition Measurement*) Adapted from Subjective Norm

The person's perception that most people who are important to him think he should or should not perform the behavior in question.

1. People who influence my behavior think I should use Internet banking. 2. People who are important to me think that I should use I-banking facilities


Social Factors

The individual's internalization of the reference group's subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations.

3. The bank staff is helpful in the use of Internet banking system. 4. The bank branch encourages Use of internet banking services.


*Measurement of this variable is on a five point Likert scale, indicating strength of agreement, ranging from (1) “strongly disagree” to (5) “strongly agree”. Venkatesh et al., (2003), argued that the social circle of an individual has an influence on their behavior. Accordingly, a third hypothesis is posited: H3: Social influence (SI) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria.


30

3.1.1.4 Operationalization of Facilitating Conditions Table: 8 Operationalization of Facilitating Conditions

Facilitating Conditions: (Hypothesis – 4) Code - FC Construct Operational Definition Measurement*) Adapted from Perceived Behavioral Control

Reflects perceptions of internal and external constraints on behavior and encompasses self-efficacy, resource facilitating conditions, and technology facilitating conditions.

1. I have knowledge necessary to use internet banking 2. I have access to computers and internet.


Compatibility The degree to which an innovation is perceived as being consistent with existing values, needs, and experiences of potential adopters.

3. I think that internet banking fits well with the way I like to work.


Facilitating Conditions

Objective factors in the environment that observers agree make an act easy to do, including the provision of computer support.

4. All the contents of internet banking service are easy to read and understand.


*Measurement of this variable is on a five point Likert scale, indicating strength of agreement, ranging from (1) “strongly disagree” to (5) “strongly agree”. The UTAUT model proposes that facilitating conditions (FC) affects the actual use behavior (Venkatesh et al., (2003), other researchers have however proposed links between facilitating conditions and behavioral intention (BI). Therefore a fourth hypothesis is proposed in order to test facilitating condition as a factor that influences BI: H4: Facilitating Conditions (FC) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria.

3.2 Model of Trust. Mayer et al. (1995) defines trust as a party’s willingness to be vulnerable to the actions of each other and they note that this will be based on the expectation that the other will perform a particular action that is important to the trustor. They further propose a model of four factors to measure trust, which includes: Ability, Benevolence, Integrity and Trustors Propensity.


31

Bhattacherjee (2002) agree to this and note the importance of trust as a key facilitator of electronic commerce; he also proposed a scale that that is based on the three key dimensions of trusts i.e. trustee's ability, benevolence, and integrity. While leaving out the last factor from Mayer et al. which is trustors propensity. The operationalization of trust will adopt Mayers et al.’s original four factors.

3.2.1 Operationalization of Trust Table: 9 Operationalization of Trust

Trust: (Hypothesis -5) Code - TR Constructs Operational Definition Measurement*) Adapted from

Ability A group of skills, competencies, and characteristics that enable a party to have influence within some specific domain

1. The bank is competent in providing Internet banking services.

Mayer et al (1995) Bhattacherjee (2002)

Benevolence

This is the extent to which a trustee is believed to want to do good to the trustor, aside from an egocentric profit motive.

2. Internet banking site is interested in my well-being, not just its own.


Integrity The relationship between integrity and trust involves the trustor's perception that the trustee adheres to a set of principles that the trustor finds acceptable.

3. I would characterize Internet banking site as honest.


Trustors Propensity

This is a reflection of how much trust one has for a trustee prior to data on that particular party being available.

4. Internet banking has enough specialists to detect fraud and information theft

Mayer et al (1995)

*Measurement of this variable is on a five point Likert scale, indicating strength of agreement, ranging from (1) “strongly disagree” to (5) “strongly agree”. Trust (TR) has been noted by various social psychology researchers as one strong factor that influences users’ attitude towards ecommerce adoption. However Lee et al., (2010), while noting that trust has a direct positive impact on behavioral intention (BI), also state that trust has been omitted in the original UTAUT model. To test the effect of trust, hypothesis 5 is proposed:


32

H5: Trust (TR) has a positive effect on the customer’s behavioral intentions to adopt Internet banking in Nigeria.

3.3 Behavioral Intentions The measurement for behavioral intentions was adapted from the three measurements of BI in UTAUT as suggested by Venkatesh et al., (2003) and is presented in Table 11.

Table: 10 Operationalization of Behavioral intentions Behavioral Intention: Code - BI

Variable Operational Definition Measurement*) Adapted from

Behavioral Intentions

The degree of users willingness to use internet banking

1. I intend to use internet banking service in the near future


2. I predict I would use the internet banking service in the future


3. I plan to use internet banking service in the near future.


*Measurement of this variable is on a five point Likert scale, indicating strength of agreement, ranging from (1) “strongly disagree” to (5) “strongly agree”.

3.4 Emerged Conceptual Framework Defining your concepts and creating a conceptual framework are means of simplifying the research task. These two processes help you clear away all the issues and materials that are not germane to your topic and research question and they also provide a ‘map’ of your field of study (Fisher, 2004). The different frameworks reviewed in the preceding sections of this chapter resulted in this conceptual framework, which will form the basis for data collection and analysis.


33

As noted earlier, the operational definitions for the key constructs in the proposed model were adopted from previously validated sources like Venkatesh et al., (2003) and Mayer et al., (1995). Figure 7: Conceptual Framework Source: Authors Diagram

Behavioral Intentions

Performance Expectancy

Effort

Social Influence

Facilitating Conditions

Trust


34

Chapter Four - Methodology The previous chapter discussed the conceptual framework and based for data collection. In this section the methodological choice made for this thesis will be described. This chapter is the overview of the research/approach and describes the choice of topic, data collection method, and literature search as well as the analysis method. It will also explain the method used to validate the instrument as well as the critique of the research work.

4.1 Research Purpose Three types of research purpose i.e. descriptive, exploratory and explanatory research methods were considered in writing this thesis. Saunders, Lewis & Thornhill (2009) explained that, in conducting a descriptive research, it is necessary to have a clear picture of the phenomena on which you wish to collect data prior to the collection of the data. Exploratory research method, on the other hand, is particularly useful if you wish to clarify your understanding of a problem, such as if you are unsure of the precise nature of the problem. Finally, explanatory research places emphasis on studying a situation or a problem in order to explain the relationships between variables. (Ibid)

This research is trying to describe customers’ acceptance of Internet banking by identifying factors that explain their intention to use Internet banking services. After consideration of the research topic and focus, the research purpose of this thesis is both descriptive and explanatory.

4.1.1 Research Approach According to Ghauri, Gronhaug & Kristianslund (1995) research methods refer to the systematic, focused and orderly collection of data for the purpose of obtaining information from it, to solve/answer our research problems or questions. There are two types of research approach; qualitative and quantitative.

• Qualitative Approach

Qualitative research usually emphasizes words rather than quantification in the collection and analysis of data. It is inductive, constructionist and interpretive (Bryman, 2004) and permits the evaluator to study selected issues in-depth and detail (Bryman & Burgess, 1999). Thus, qualitative research is common in social and behavioral sciences, and among practitioners who want to understand human behavior and functions. It is suitable for studying


35

organizations, groups and individuals. In qualitative research, findings are not arrived at by statistical methods or other procedures of quantification.

• Quantitative Approach

Quantitative research entails the collection of numerical data as exhibiting a view of the relationship between theory and research as deductive, a preference for a natural science approach (and of positivism in particular) and as having an objectivist conception of social reality, this type of research can be characterized as linear series of steps moving from theory to conclusions, and its measurement process entails the search for indicators (Bryman, 2004). A quantitative approach also requires the use of standardized measures so that the varying perspectives and experiences of people can be fit into a limited number of predetermined response categories to which numbers are assigned (Bryman and Burgess, 1999).

The major difference between qualitative and quantitative research is not ‘quality’ but procedure (Ghauri et al., 1995). Illustrated in Table 11 below are the differences between qualitative and quantitative research methods.


36

Table 11: Comparison of the Quantitative and Qualitative Research Methods Qualitative Methods Quantitative Methods

Emphasis on understanding Emphasis on testing and verification

Focus on understanding from respondent’s/informant’s point of view

Focus on facts and/or reason of social events

Interpretation and rational approach Logical and critical approach

Observations and measurement in natural settings Control measurement

Subjective ‘insider view’ and closeness to data Objective ‘outsider view’ distant from data

Explorative orientation Hypothetical-deductive; focus on hypothesis testing

Process oriented Result oriented

Holistic perspective Particularistic and analytical

Generalization by comparison of properties and context of individual organism

Generalisation by population membership

Source: Ghauri et al., (1995)

The purpose of this research therefore, will be to describe customers’ acceptance of internet banking by identifying factors that explain their intention to use internet banking services. To achieve this purpose I chose a framework and developed a research hypothesis. Data will be collected from sample customers and analyzed. Ghauri et al., (1995) noted that quantitative method is best suited when emphasis is on testing and verifying a hypothesis. Therefore quantitative analysis research method will be used in conducting this study.

4.1.2 Research Strategy Yin (2003), discusses five different research strategies: experiment, case study, survey, archival analysis and history. The selection of appropriate research strategy should be based


37

on types of research questions, the extent of control over behavioral events, and the degree of focus on contemporary events (ibid.)

The strategy chosen for doing this study is survey approach. A survey is defined by Saunders et al., (2009, P.601) as ‘‘a research strategy that involves the structured collection of data from a sizeable population.” They conclude that the term ‘survey’ often is used to describe the collection of data using questionnaires.

4.2 Sample Selection In the previous part of the discussion, a research strategy was selected for the study. The next step is to clearly identify how the survey will be carried out. Using this strategy, an appropriate study sample must be selected to answer the research questions.

A sample is a subset of a population and sampling is the process of choosing an appropriate number of this population in order to make it possible to do generalizations for the study (Sekaran, 2000). Using a sample rather than examining an entire population for a study is fairly obvious regarding time, cost and human resources (Ibid). Samples are divided into two main groups: probability and non-probability samples.

4.2.1 Probability Sampling Saunders et al. (2009) note that Probability sampling (or representative sampling) is most commonly associated with survey-based research strategies where you need to make inferences from your population sample, to answer your research question(s) or to meet your objectives. They further state that the process of probability sampling can be divided into four stages:

1. Identify a suitable sampling frame based on your research question(s) or objectives.

2. Decide on a suitable sample size.

3. Select the most appropriate sampling technique and select the sample.

4. Check that the sample is representative of the population.


38

4.2.2 Non-probability Sampling Non-probability sampling (or non-random sampling) provides a range of alternative techniques to select samples based on subjective judgment and is also very useful when the resources available for conducting the research are limited (Saunders et al., 2009).

The sample selection used for this study is a non-probability sampling and with a mix of judgmental and convenience sampling. Judgmental sampling is, according to Sekaran (2000), when informants are selected on the basis of their expertise in the subject investigated, and this will be done by selecting the banking customers that do transaction with the banks; the study will also take into consideration the self-selection sampling. Saunders et al. (2003) note that self-selection sampling occurs when you allow each case, usually individuals, to identify their desire to take part in the research. You therefore:

1. Publicize your need for cases, either by advertising through appropriate media or by asking them to take part.

2. Collect data from those who respond (Ibid)

Saunders et al. (2003) further stated that publicity for convenience samples can take many forms. These include articles and advertisements in magazines that the population is likely to read, postings on appropriate Internet newsgroups and discussion groups, hyperlinks from other websites as well as letters or emails of invitation to colleagues and friends (Ibid). The respondents for this research were selected from different intellectual and social background to answer the questionnaire.

4.3 Data Collection Method and Analysis When writing a thesis it is important to make a general decision about the research method to be used (Fisher, 2007). In other words all available method should be considered and some form of elimination process used to find a suitable method or methods that will be used (Ibid).

According to Saunders et al. (2007), there are two approaches to collecting data for a project and these are: primary data and the secondary data. They further explained that, primary data is collected basically when a particular purpose arises whereas secondary data are already collected data, which has been published, and for which new researchers can rely on as a source of information.


39

4.3.1 Primary Data Primary data are first-hand information data collected specifically for the research project being undertaken (Saunders et al., 2009). This could be in the form of an interview, records written and kept by people involved in, or who bear witness to an event (Burns, 2000). Primary data can be collected through observation, interviews, or the use of questionnaires (Saunders et al., 2009). For this study, primary data were gathered through a questionnaire that was administered on the actual customers of the banks.

4.3.2 Questionnaire Saunders et al., (2003), define questionnaires as including structured interviews and telephone questionnaires as well as those in which the questions are answered without an interviewer being present. They further argued that the questionnaire is one of the most widely used data collection techniques within the survey strategy. Because each person (respondent) is asked to respond to the same set of questions, it provides an efficient way of collecting responses from a large sample prior to quantitative analysis (Ibid). A 37 item survey instrument was developed to achieve three research goals. First was to identify the level of the respondent’s computer and internet knowledge. Secondly the demographics of participants were noted. Finally, the survey included a series of items to identify participants’ perceptions related to their intention to use internet banking as outlined by the constructs of Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). Relevant items were adapted for the context of internet banking activity based on those developed by Venkatesh et al., in a cross validation test of the UTAUT. The instrument was first tested on five individuals and based on their feedback minor wording changes were made, before implementing the survey with the entire sample population (see Appendix 2) An e-questionnaire was developed and delivered to the target population online, through the various Nigerian centered forums and through personal invitation by emails. Park et al. (2007) noted that online web survey can be used as an efficient and useful means to study consumer behaviors as it relates to information technology such as the Internet and the mobile technology


40

4.3.3 Secondary Data Secondary data is data from existing information such as published books, articles, internet search engines etc. One of the benefits of collecting secondary data is that it saves time and cost for the researcher since the researcher uses existing information. Another major advantage of this process is that it serves as guide and aid on how to conduct the research. (Saunders et al., 2003)

According to Ghauri et al., (1995), one of the main disadvantages of using secondary data is that these data are collected for another study with different objectives and may not completely fit the current problem.

To support this study, articles published by other researchers in the area of internet banking were used extensively.

4.4 Literature Search Literature, relevant to the purpose of this study, was found through different sources, such as, articles in academic journals contained in EMERALD data base in Luleå Tekniska Universitet, and search engines such as LUCIA, Business source elite and JSTOR-LTU,. In addition, to the Internet, with search tools like Google Scholar and search engine for online publications, were used. In the search for relevant literature, key words like ‘banking’, ‘internet banking service’, ‘e-commerce’ etc. were used.

4.5 Analysis Method Yin (1994) stated that data analysis is the examining, categorizing, tabulating or otherwise recombining of the collected data. The purpose of analyzing data is to find answers to questions and link information from a mass of data (Ibid). This research will combine data from multiple sources and the data collected will be analyzed and linked together to establish a pattern or an idea.

The data collected was analyzed with SPSS ver20 using descriptive statistics such as frequencies, percentages and chi square test at a level of significance at probability level of 5%. Descriptive statistics are statistics that can be used to describe variables or generalize information from a sample (Saunders et al., 2009; Fisher, 2010). It summarizes the information in a collection of data (Agresti & Finlay, 2009). Descriptive statistics was used in this study to summarize the data collected with the questionnaire. After the analysis using

http://xerxes.its.ltu.se/list/databases/proxy/LIB33242


41

descriptive statistics, a test of Pearson moment correlation was conducted to measure the relationship between the different variables (Fisher, 2010). Pearson moment correlation is a “statistical test that assesses the strength of the relationship between two numerical data variables” (Saunders et al., 2009, P.597). It is necessary therefore, to use this test in this study to see the strength of the relationship between the different variables. The instrument used to gather data was validated using exploratory factor analysis (EFA) in SPSS. Exploratory factor analysis (EFA) is an orderly simplification of interrelated measures (Suhr, 2006). EFA was used to simplify the questionnaire, in other to see the relatedness of the different questions to each other. A multiple linear regressions as well as simple linear regression were done to test the proposed hypothesis. Regression analyses are suitable when examining strength of relationship between a dependent variable and one or more independent variables (Saunders et al., 2003). Multiple linear regressions were suitable in the analysis of this research since the purpose of the research was aimed at identifying multiple factors that affects Behavioral intentions to adopt internet banking. Multiple linear regression analysis was considered for this test due to its robustness and the ability to analyze even small sample size of the data. Table 12 below compares the difference between Linear Regression, Partial Least Square (PLS) and LISREL.


42

Table: 12 Comparative Analyses between Techniques Issues LISREL PLS Linear Regression Objective of Overall analysis

Show that the null Hypothesis of the entire proposed model is plausible. While rejecting path- specific null hypothesis of no effect.

Reject a set of path Specific null hypotheses of no effect

Reject a set of path Specific null hypotheses of no effect

Objective of Variance Analysis

Overall Model fit such as Insignificant X2 or high AGFI

Variance Explanation ( High R Square)

Variance Explanation ( High R Square)

Required Theory Base

Requires Sound Theory base. Supports Confirmatory research

Does not necessarily require sound theory base. Supports both exploratory and confirmatory research.

Does not necessarily require sound theory base. Supports both exploratory and confirmatory research.

Assumed Distribution

Multivariate Normal if estimation is through ML. Deviations from Multivariate normal is supported with other estimation techniques.

Relatively Robust to deviations from a multivariate distribution.

Relatively Robust to deviations from a multivariate distribution, with established method of handling non multivariate distributions

Required Minimal Sample Size

At least 100 - 150 At least ten times the number of items in the most complex constructs.

Supports smaller sample sizes, although a sample of at least 30 is required.

Source: Adopted from Baraghani (2007, P.76 while quoting Geffen 2000)


43

4.6 Method critique Bryman (2004) note that research works can be too subjective. Therefore, findings can be influenced by the researcher’s mostly unsystematic views (i.e. what the researcher sees as being significant and important can sometimes be a function of the relationships, personal or otherwise that the researcher strikes up with the subjects that are being investigated).

The author therefore reiterate as stated in my limitations, that this research albeit specific cannot be used to generalize and make an assumption that it will be true for all consumer groups.

4.7 Reliability and Validity According to Holme & Solvang (1996) as quoted by Stambro and Svartbo (2002) the goodness of data can be measured in reliability and validity.

4.7.1 Reliability The reliability of data if collected will yield consistent findings, similar observations would be made or conclusions reached by other researchers (Easterby-Smith, Thorpe, Jackson & Lowe, 2008). If the data collected does not serve the purpose of collecting it, then it is termed to be irrelevant to the investigation (Ibid). Furthermore it is important to ask these three questions when checking for the reliability of the data collected;

1. Will the measures yield the same results on other occasions?

2. Will similar observations be reached by other observers?

3. Is there transparency in how sense was made from the raw data? (Easterby-Smith et al., 2008)

The reliability of the instrument used for data collection was verified using the Cronbach Alpha test. Cronbach’s alpha is a measure of internal consistency reliability (Gliem & Gliem, 2000). It is a “technique that requires only a single test administration to provide a unique estimate of the reliability for a given test. Cronbach’s alpha is the average value of the reliability coefficients one would obtain for all possible combinations of items when split into two half-tests.” (Ibid, P.84)


44

4.7.2 Validity Validity according to Saunders et al. (2009) is concerned with whether the findings are really about what they appear to be about. Is the relationship between two variables a causal relationship?

Construct validity refers particularly to research that uses questionnaires or inventories to assess whether a person or an organization exhibits a particular characteristic (Fisher, 2010).

The internal validity and reliability of the data you collect and the response rate you achieve depend, to a large extent, on the design of your questions, the structure of your questionnaire (Saunders et al., 2003). In designing the questionnaires certain factor were taken into consideration. These factors are;

1. Characteristics of the respondents from whom you wish to collect data 2. Importance of reaching a particular person as respondent 3. Importance of respondents’ answers not being contaminated or distorted 4. Size of sample you require for your analysis, taking into account the likely response

rate 5. Types of question you need to ask to collect your data 6. Number of questions you need to ask to collect your data (Saunders et al 2003).

Content validity of the questionnaire for this research was ensured through careful selection and adoption of items from previously validated instruments (Venkatesh et al, 2003; Foon and Fah, 2010; Dong et al., 2008; Al-Qeisi, 2009). Besides this the validity was also verified using an exploratory factor analysis. As stated before, the questionnaire was tested on five bank customers. The feedback from the pilot test was used to improve the readability and the quality of the questions in the instrument.


45

Chapter Five – Data Presentation and Analysis This part consists of the collected empirical data. The findings will be presented in this chapter and will subsequently be used in the analysis. As explained earlier in chapter four the data for this chapter was gathered using questionnaires which were sent out to banking customers of banks in Nigeria. This chapter is arranged in three parts (1) a descriptive analysis are presented to characterize the data collected. (2) The measurement model is checked for reliability and validated. (3) Multiple and Simple Linear regression analysis is done to test the hypotheses.

5.1 Demographics and Descriptive Statistics As stated earlier descriptive statistics summarizes the information in a collection of data (Agresti & Finlay, 2009). Descriptive statistics consists of graphs, tables and numbers such as averages and percentages (Ibid). The target population for this study is the customers of the banks, who are either using or not using the internet banking facilities offered by various banks in Nigeria. The Statistical Package for Social Sciences (SPSS) version 20 was used to screen and analyze the data collected. SPSS software according to Kuzic (2002) while quoting (Ghauri et al, 1995; Cramer, 1998) is a widely accepted package for conducting analysis in social sciences A total of 56 responses were received with 16 of the responses representing 28.6 percent of the total responses received being incomplete. See table 13 below.


46

Table: 13 Frequencies and Percentages Variable N Measurement Frequency Percentage %

Internet Banking User 40

Yes 18 45 Yes but not regularly 13 32.5 No 9 22.5

Computer knowledge 40

very good 19 47.5 good 17 42.5 moderate 3 7.5 poor 1 2.5 very poor 0 0.0

Internet knowledge 40

very good 19 47.5 good 17 42.5 moderate 3 7.5 poor 1 2.5 very poor 0 0.0

Gender 40 Male 29 72.5 Female 11 27.5

Age 40 21- 30 10 25 31- 40 28 70 41 -50 2 5

Education 40

higher education 19 47.5 bachelor 18 45 diploma 2 5 other 1 2.5

Type of employment 40

free lancing 2 5 Private Sector 18 45 Public sector 10 25 my own business 7 17.5 Not working 3 7.5

The general demographics of the respondents illustrated in table 13 above shows that out of the 40 complete responses received, 29 or 72.5% were male and 11 or 27.5% were female. From table 13 above, 70% or 28 of the respondents falls within the age group of 31- 40, followed by the 21-30 group, which represents 10 people or 25% of the total respondents. Almost half of the respondents about 45% said that they use internet for banking, 13 respondents use internet for banking but not regularly while 9 respondents representing 22% do not use internet banking services. 47.5% representing 19 respondents are very good at the


47

use of internet and also computers, 17 respondents or 42.5% are good at the use of internet and also computers.

5.1.1 Chi Square Tests Pearson Chi-Square test of independence was carried out at a predetermined alpha level of 0.05 to see the relationship between the different variables and Internet banking usage. The result is explained and illustrated in tables 14 – 17.

Table: 14 Chi-Square Tests for Internet Knowledge and the effect on Internet banking

usage

Value df Asymp. Sig. (2-

sided)

Pearson Chi-Square 14.178a 6 .028

Likelihood Ratio 13.123 6 .041

Linear-by-Linear Association 1.677 1 .195

N of Valid Cases 40

a. 8 cells (66.7%) have expected count less than 5. The minimum

expected count is .23. The table 14 above is a Chi Square test of the relationship between the knowledge of Internet of our respondents and their use of internet banking. The Pearson Chi-Square value for test of independence was 14.178 at 6 degree of freedom and a significance of 0.028, which is below the 0.05, that is the threshold to accept or reject a null hypothesis. A statistically significant result has a probability of less than .05 (Wielkiewicz, 2000). The descriptive statistics in table 13 showed that 90 percent of the respondents have either good or very good knowledge of the internet in contrast to 10 percent with moderate or poor knowledge. Judging by the Chi-Square value and significance, there is a significant difference between the two variables of knowledge of internet and behavioral intention to use. Therefore, it could be inferred that there is a positive relationship between the two variables of knowledge of internet and use of internet for banking. Simply put there seem to be a correlation between the respondents’ knowledge of the internet and he intention to use internet banking services. Table 15 below illustrates the result of the Chi-Square test of independence of the relationship between the type of employment of the respondents and their use of internet for banking. The


48

result returned a Chi-Square value of 2.7 at 8 degrees of freedom and a significance of 0.947. This is more than the 0.05 the threshold to accept or reject a null hypothesis (Ibid). This therefore shows that there is no significant difference between the two variables and so no relationship between the respondents’ type of employment and their use of the internet for banking.

Table: 15 Chi-Square Tests for Employment and the effect on Internet banking.


sided)



Linear-by-Linear Association .023 1 .880

N of Valid Cases 40


expected count is .45.

The Chi-Square test for independence of gender and use as shown in table 16 below indicated that there is possibly no influence of the gender of the respondents to their use of internet for banking. The result of the test returned a value of 4.8 at 2 degrees of freedom and a significance of 0.089 and this is above the 0.05 which is the threshold for acceptance or rejection of a null hypothesis (Ibid).


49

Table 16: Chi Square test of Gender and the effect on Internet Banking Usage


sided)




N of Valid Cases 40


expected count is 2.48.

Table 17 below illustrates the Chi-Square test of independence of the respondents Knowledge of computers and its relationship to their use of internet banking services. The test showed that there was possibly no effect of the respondents’ general knowledge of computers on their use of internet banking. The result of the test at significance of 0.516 and 6 degrees of freedom is more than the 0.05 level which is the threshold for acceptance or rejection of a null hypothesis (Ibid). There is therefore no significant difference between the two variables of computer knowledge and use of internet banking and from the result a relationship cannot be inferred.


50

Table 17 Chi Square test of Computer Knowledge and the effect on Internet Banking Usage


sided)




N of Valid Cases 40


expected count is .23.

5.2 Validation of Measurements Before testing the Hypothesis the research instrument was assessed for its reliability as well as construct validity. An exploratory factor analysis (EFA) was done to validate both the discriminant and convergent validity of the questionnaire and the variables. Then Cronbach’s coefficient alpha was computed for each variable to test for reliability. Finally a descriptive statistics of the final items included in the model was computed to show the mean, standard deviation, skewness and kurtosis of the sample data

5.2. 1 Exploratory factor Analysis The 23 behavior estimation items were factor analyzed in two batches with a sample of 40 bank customers, using principle components analysis (PCA) for factor extraction. The first batch of the analysis consisted of three variables namely Performance Expectancy (PE), Effort Expectancy (EE), and Social Influence (SI). The second batch also consisted of three variables i.e. Facilitating Condition (FC), Trust (TR), and Behavioral Intention (BI). The preliminary analysis of the first batch indicated moderately high factorability – Bartletts test was significant at p < .000. This indicates that correlations were adequate to conduct factor analysis (AbuShanab and Pearson, 2007) and the overall value of Kaiser-Meyer-Olkin test


51

was .74. The recommended value of KMO is greater than 0.5 (Polite, 2010; AbuShanab & Pearson, 2007). Using minimum eigenvalue of 1.0 as the extraction criterion for factors, three factors that accounted for a total of 78.1% of the variance were extracted. Communialities were fairly ranging from .57 to .89 as shown in table 18. More common magnitudes in the social sciences are low to moderate communalities of .40 to .70 (Costello and Osborne, 2005). The three factors were orthogonally rotated using varimax. The items in table 18 are ordered by variables to facilitate interpretation of the factor matrix. Table: 18 first batch of Factor Analysis

Factor Loadings Communalities

Variable 1 2 3 H2

PE2 - .680 .663

PE3 - .883 .800

EE1 .886 - - .839

EE2 .718 .419 .724

EE3 .922 - .892

EE4 .894 - .801

SI1 - .938 - .885

SI2 - .926 .863

SI4 - .727 .567

Eigenvalue 3.9 2.1 1.0 Percentage of Variance Explained

35 27 17

Note: Varimax was used for factor rotation and factor loadings below 0.40 are not shown in the table


52

Overall the factor structure that emerged was reasonably clear and interpretable. Items PE1, PE4 and SI3 were eliminated from the final model due to either high cross loadings or loading highly on a different factor that it is supposed to. However EE2 is accepted even though it was cross loading on two factors. When an item loads highly on two factors, a difference of .20 or higher is required to accept the construct to a factor (Polit, 2010). Since EE2 was loading highly on factor 1 and there is a difference of .29 from loadings on factor 3. EE2 is therefore accepted to factor 1. See table 18

The second batch of analysis was done for the variable Facilitating Condition (FC), Trust (TR) and Behavioral Intention (BI), using the same settings as the first batch of the analysis. The result indicates that Bartletts test is significant at p < .000 and the overall value of Kaiser-Meyer-Olkin test is 76. The communalities range from .67 to .90. See table 19. Two items TR1 and TR2 is deleted from the final model due either high cross loading or loading highly on a different factor that it is not supposed to. FC4 loaded on both factor 2 and 3, however due to a difference of over .30 between the two loadings FC4 is accepted to Factor 2 (Polite, 2010).


53

Table: 19 Second Batch of Factor Analysis

Factor Loadings Communalities

Variables 1 2 3 H2

FC1 - .785 - .755

FC2 - .822 - .785

FC3 - .702 - .675

FC4 - .749 .442 .758

TR2 - - .836 .768

TR4 - - .824 .721

BI1 .917 - - .906

BI2 .920 - - .900

BI3 .936 - - .963

Eigenvalue 4.6 1.6 1.0

Percentage of Variance Explained % 31 29 20

Note: Varimax was used for factor rotation and factor loadings below 0.40 are not shown in the table

Composite scores were created for each of the six factors, based on the mean of the items which had their primary loadings on each factor. Performance expectancy (PE) is the influencing factor with the highest mean value, and a negatively skewed distribution, whilst the rest of the factors included in the model besides Trust (TR) also had negative skewness distributions. Trust had a positively skewed distribution. Social Influence had the lowest mean and the most negatively skewned distribution. Descriptive statistics are presented in Table 20 below. Skewness and kurtosis statistics are very dependent on the sample size and smaller sample sizes can be misleading (McNeese, 2008). The skewness and kurtosis were well within a tolerable range for assuming a normal distribution based on the sample size, and an


54

examination of the histograms suggested that the distributions looked fairly normal (see Appendix E).

Table 20 Descriptive Statistics of Items included in the Model

N Range Mean Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Std.

Error Statistic Std.

Error PE 40 2,50 4,4250 ,70302 -1,002 ,374 ,104 ,733 EE 40 2,75 4,2563 ,79156 -,927 ,374 ,142 ,733 SI 40 4,00 3,1667 1,14976 -,160 ,374 -,586 ,733 FC 40 3,50 4,2688 ,78933 -1,451 ,374 2,532 ,733 TR 40 3,00 3,3250 ,95776 ,345 ,374 -,686 ,733 BI 40 4,00 3,8917 1,23433 -,938 ,374 -,030 ,733 Valid N (listwise)

40

5.2.2 Reliability The most common method to measure the reliability of Likert scales is Cronbach's Alpha” (Dong et al, 2008). Cronbach alpha analysis was performed on all the accepted variables to ensure the reliability of the questionnaire and to test for substantial flaws which can be created while a survey is been developed. The result shows a satisfactory loading for all the six variables analyzed. See table 21 below: Values that have been used in the literature as acceptable Cronbach’s alpha range from 0.6 and above (AbuShanab and Pearson, 2007; Wu & Wang, 2005). Table: 21 Cronbach’s Analysis for Reliability

Variable Factor Loading PE .630 EE .909 SI .835 FC .841 TR .693 BI .958


55

5.3 Test of Model and Hypothesis Given the relatively small sample size, the testing of hypothesis and model is done using multiple linear regressions to measure the relationship between the variables. “Regression has become one of the most widely used techniques in the analysis of data in the social sciences. It should become apparent that regression is a powerful tool for summarizing the nature of the relationship between variables and for making predictions of likely values of the dependent variable” (Bryman and Cramer, 2001). Regression analysis is also used to predict the value of a dependent variable from one or more independent variables (Saunders et al., 2009).

5.3.1 Multiple Linear Regression Analysis Using a specific procedure in SPSS, a multiple regression was done to test the hypothesis proposed in the study; the result is shown in Tables 22-23. From table 23 it could be seen that the model is not significant and only about 33% of the variation in the BI was explained by the model leaving 67% unexplained. Using table 23 a literary deduction from the regression analysis is the model below, which can be used to estimate BI when the variables (constructs) are given. The estimation of the coefficients is based on the collected observation. The significance of the coefficients should be tested to ensure an accurate and precise prediction of BI. This is done later in this work. BI = -0.996 +0.53PE+ 0.271EE+0.099SI+0.155FC+0.124TR Table: 22 Model Summary of Multiple Regressions Analysis

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate 1 .572a .327 .228 1.09167

a. Predictors: (Constant), TR, EE, SI, PE, FC


56

Table: 23 Multiple Regressions Coefficient

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

Correlations Collinearity Statistics

B

Std. Erro

r Beta Zero-order Partial Part Tolerance VIF

1 (Constant)

-.996 1.236

-.805 .426

PE .530 .320 .302 1.657 .107 .493 .273 .233 .597 1.676

EE .271 .334 .174 .812 .422 .432 .138 .114 .431 2.318

SI .099 .180 .093 .553 .584 .262 .094 .078 .707 1.414

FC .155 .417 .099 .370 .713 .493 .063 .052 .278 3.601

TR .124 .250 .097 .499 .621 .316 .085 .070 .528 1.895

However checking table 23 for significance of the coefficients of the predictor variables, it can be seen that none of the coefficients of the variables is significant at 10% significance level. The non-significance observed in this multiple regression could be due to the limitation of few observations in this study. The minimal number of cases for reliable results is more than 100 observations and 5 times the number of items. This observation is supported by the conclusion in the following references (Suhr, 2006; Habing, 2003). De Winter, Dodou & Wieringa (2009) notes a minimum of 50 observations for an exploratory factor analysis. The major focus of this study however is to test the influence of the five constructs on BI and not specifically to create a model for the prediction of BI. If the latter is to be done, more observations are necessary for accurate and precise predictions. Looking at the correlation matrix in appendix D, the coefficients of PE, EE, FC and TR will most likely be significant and contribute to accurate prediction of BI if large sample size is investigated. On the other hand SI might not have substantial contribution in predicting BI as the correlation with BI is very low and also not significant. This is further discussed later in this report under simple regression analysis.

5.3.2 Simple Linear Regression A simple linear regression analysis is further done in SPSS to investigate the hypotheses of this thesis, since a conclusive inference could not be drawn from the multiple regression analysis.


57

The result of the simple linear regressions analysis, using each construct separately is shown in table 24-29. The ANOVA test for the simple regression models using each construct shows that the models for PE, EE, FC and TR are statistically significant since P value is less than 0.05. On the other hand SI is not statistically significant since its P value is greater than 0.05. The same inference can be drawn if the coefficient of the regressor is checked in the analyses (See table 29). In each model, the coefficient of the regressors are significant (i.e. p<0.05) except for the model of SI where the coefficient is not significant. In simple statement, the indication of this analysis is that SI does not have influence on the behavioral intention to adopt internet banking among the selected respondents of the survey. The explanation could be that banking decisions and actions are considered to be confidential and private concerns and hardly discussed as social issues in Nigeria. It is one of the sensitive issues which are hardly influenced by social characteristics for some reasons. It may also be because of a declining role of social influence under discretionary usage and increased experience conditions. This inference is also discussed and is consistent with observations in previous researches (Bankole, Bankole & Brown, 2011; Al-Qeisi, 2009). Table: 24 ANOVA calculations for – PE

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression 14.426 1 14.426 12.183 .001b

Residual 44.994 38 1.184

Total 59.419 39

a. Dependent Variable: BI

b. Predictors: (Constant), PE


58

Table: 25 ANOVA calculations for – EE ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 11.066 1 11.066 8.696 .005b

Residual 48.354 38 1.272

Total 59.419 39


b. Predictors: (Constant), EE

Table: 26 ANOVA calculations for – SI

ANOVAa


1

Regression 4.078 1 4.078 2.800 .102b

Residual 55.341 38 1.456

Total 59.419 39


b. Predictors: (Constant), SI Table: 27 ANOVA calculations for – FC

ANOVAa


1

Regression 14.465 1 14.465 12.228 .001b

Residual 44.954 38 1.183

Total 59.419 39


b. Predictors: (Constant), FC


59

Table: 28 ANOVA calculations for – TR ANOVAa


1

Regression 5.938 1 5.938 4.219 .047b

Residual 53.481 38 1.407

Total 59.419 39


b. Predictors: (Constant), TR

It should be noted that a model to predict the behavioral intention of any observation will not be a simple regression model of the different constructs, but rather a multiple linear regression.

Table: 29 Beta Weight Calculations for the Simple Regression Test

Performance Expectancy (PE) Coefficientsa

Model


Standardized Coefficients

t Sig.


B Std. Error Beta

Zero-order Partial Part Tolerance VIF

1 (Constant) .064 1.110 .057 .955

PE .865 .248 .493 3.490 .001 .493 .493 .493 1.000 1.000

Effort Expectancy (EE) Coefficientsa

Model



t Sig.


B Std. Error Beta


1 (Constant) 1.027 .988 1.040 .305

EE .673 .228 .432 2.949 .005 .432 .432 .432 1.000 1.000


60

Social Influence (SI) Coefficientsa

Model



t Sig.


B Std. Error Beta


1 (Constant) 3.001 .565 5.308 .000

SI .281 .168 .262 1.673 .102 .262 .262 .262 1.000 1.000

Facilitating Conditions (FC) Coefficientsa

Model



t Sig.


B Std. Error Beta


1 (Constant) .598 .957 .625 .536

FC .772 .221 .493 3.497 .001 .493 .493 .493 1.000 1.000

Trust (TR) Coefficientsa

Model



t Sig.


B Std. Error Beta


1 (Constant) 2.537 .686 3.700 .001

TR .407 .198 .316 2.054 .047 .316 .316 .316 1.000 1.000


In summary the test of hypothesis using the multiple regression analysis rejected the entire proposed hypothesis for all the variables. However using the simple regression analysis the results indicates that the hypothesis proposed for the following variables performance expectancy (PE), effort expectancy (EE), facilitating condition (FC) and trust (TR) were accepted. Social influence (SI) was however, rejected. Table 30 below shows a summary of the results.


61

Table: 30 Summary of the Test of Hypothesis

Hypothesis Multiple Reg. Analysis

Simple Reg. Analysis

H1 Performance Expectancy (PE) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria.

Rejected Accepted

H2 Effort Expectancy (EE) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria

Rejected Accepted

H3 Social influence (SI) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria.

Rejected Rejected

H4 Facilitating Conditions (FC) has a positive influence on the customers’ behavioral intentions to adopt Internet banking in Nigeria.

Rejected Accepted

H5 Trust (TR) has a positive effect on the customer’s behavioral intentions to adopt Internet banking in Nigeria.

Rejected Accepted


62

Chapter Six - Conclusion This chapter summarizes the observations from the previous chapter and presents a reflection on the results of the study as well as the academic contributions of the study and the limitations of the study. The major objective of the study is to describe customers’ acceptance of internet banking by identifying factors that explain their intention to use internet banking services in Nigeria and not necessarily to create a model. First this thesis explored the nature of internet and internet banking in Nigeria and although internet banking is a fairly new innovation in Nigeria, this thesis has been able to show that almost all the banks in Nigeria are offering some form of internet banking service. Secondly on the analysis of the demography of the respondents and the descriptive statistics the thesis is able to show that:

1. There is a relationship between the level of knowledge of internet issues and the intention of bank customers to adopt internet banking. The better the customers knows about the internet the more likely the intention of the customers will be to adopt internet banking services

2. There is no relationship between the type of employment of the customers and their intention to adopt internet banking. The employment of the customer to adopt internet banking does not depend on the type of job the customer has.

3. There is possibly no relationship between the gender of the customers of the banks and their intention to adopt internet banking. Gender also does not form a basis for intention to adopt internet banking.

4. There is possibly no relationship between the knowledge of computers of the customers of the banks and their intention to adopt internet banking. How good a customer is in the use of computers does not have any effect on the customer’s intentions to adopt internet banking in Nigeria.

Looking further at the factors that may influence customers to adopt internet banking, this thesis is not able to draw a conclusive and satisfactory inference based on the UTAUT and Trust model variables that is chosen for this analysis. As explained earlier this may be as a


63

result of the sample size being too small to impact considerably on the result. However, the result obtained from the multiple linear regression analysis showed that none of the variables from the UTAUT and trust model has a considerable influence on the intention of the respondents to adopt internet banking, even though these variables have been proven to have considerable influence by other researchers (Venkatesh et.al., 2003; Baraghani, 2007; Dong, Liu, Qian & Song, 2008; Lee et al., 2010; Dulle & Minishi-Majanja, 2011; Mayer et al., 1995; Bhattacherjee, 2002). An examination of the coefficients and correlation matrix of the multiple regression analysis is able to indicate that the coefficients of Performance Expectancy (PE), Effort Expectancy (EE), Facilitating Condition (FC) and Trust (TR) will most likely be significant and contribute to accurate prediction of Behavioral Intention (BI) if a larger sample size is investigated. On the other hand Social Influence (SI) might not have substantial contribution in predicting BI as the correlation with BI is very low and also not significant. This observation on SI is also consistent with observations by some researchers (Bankole, Bankole & Brown, 2011). Finally, a Simple linear regression analysis (even though it is not a base for measurement of this thesis) is also done to examine the regression relationship of the variables. The results were mostly consistent with Venkatesh et al., (2003) proposition on the UTAUT model and Mayer et al., (1995) model of trust. However the Social influence (SI) variable of UTAUT was still found not to be significant and therefore not consistent with the original UTAUT proposition.

6.1 Academic Contributions of the Study This study has been able to:

• Show that there is an increasing growth in the users of internet banking services by bank customers in Nigeria.

• Successfully identify some of the characteristics of the Nigerian bank customer.

The study also made contributions in the IT / IS research; one of which is the important observations on the social influence (SI) variable of the UTAUT model. This study was able to show that this variable may not conform to the generally accepted propositions of the model when it is tested in the Nigeria market.


64

6.2 Limitations / Future Research There were limitations that affected the overall writing of this thesis. First, the time frame for writing this thesis was limited and so is not enough to get responses that will ensure that a conclusive inference can be drawn from the results. Secondly the use of an online survey puts a limit of responses to only Internet users. Hence, the results may not be generalizable to non-Internet users. Thirdly, the samples of Internet users for this study were mostly those who tend to be more knowledgeable about the Internet and are thus experienced Internet users. This may also explain the result of the Chi Square test of Internet Knowledge and Intention to adopt Internet banking. Therefore, the sample of respondents may be skewed toward the more experienced Internet users. Finally, the small sample size has a profound influence on the observed result and inference that is drawn in this thesis. It is most likely possible that if a larger sample size is investigated under the same conditions, a different observation and conclusion may be reached. This however will depend on whether the sample is representative or not. This thesis is limited to identifying factors that influence customer’s intention to adopt internet banking and not to either measure the actual adoption, verify any model or create any model. Although the result and conclusion reached here is true and specific to this study, it cannot be used to generalize and make an assumption that it will be same for other behavioral intention studies. Future Research The objective of this research is achieved in the process of writing the research albeit inconclusive. However, the inconclusive results of this study indicate the need for further investigation. Further research on this topic with more sample results of up to 100 or more should be conducted to fully confirm some of the observations and assumptions that have been made on this thesis. This will also make the result generalizable to other case studies. A future research is also suggested with a concentration on the social influence variable of UTAUT, to really prove conclusively if it is still relevant in the realities of today.


65

References: AbuShanab E., J.M. Pearson, (2007),"Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective", Journal of Systems and Information Technology, Vol. 9 Iss: 1 pp. 78 - 97 Adesina Aderonke A and Ayo Charles K.(2010), An Empirical Investigation of the Level of Users’ Acceptance of E-Banking in Nigeria Retrieved from http://www.arraydev.com/commerce/JIBC/2010-04/Adesina.pdf accessed on February 11 2012 Agresti, Alan and Barbara Finlay, 2009. Statistical Methods for the Social Sciences, New Jersey, Prentice Hall. 4th ed. AHRQ Quality Indicators: Composite Measures User Guide for the Inpatient Quality Indicators (IQI) retrived from http:// www.qualityindicators.ahrq.gov accessed on May 3, 2012 Al-Qeisi K. I. (2009), Analyzing the use of UTAUT Model in Explaining an Online Behavior: Internet banking adoption. Ajzen, I. and M. Fishbein, Understanding attitudes and predicting social behaviour. 1980, Prentice-Hall, Inc. Englewood Cliffs, New Jersey Aladwani, Adel. “Online Banking: A Field Study of Drivers, Development Challenges, and Expectations,” International Journal of Information Management, 21, 2001, pp. 213–225. Ayo C. K, Adewoye J. O, & Oni A. A (2010) The State of e-Banking Implementation in Nigeria: A Post-Consolidation Review Retrieved from http://jetems.scholarlinkresearch.org/articles/e-banking.pdf accessed on februari 12, 2012 Amin (2007), Internet banking adoption among young intellectuals: Journal of Internet banking and Commerce, volume 12, No. 3 Avinandan M. and Prithwiraj N. (2003) “ A model of trust in online relationship banking” International Journal of Bank Marketing; Vol 21, iss: 1, pp 5-20

http://jetems.scholarlinkresearch.org/articles/e-banking.pdf


66

Bankole Felix O., Omolola O. Bankole, Irwin Brown, Mobile Banking Adoption in Nigeria, The Electronic Journal on Information Systems in Developing Countries, EJISDC (2011) 47, 2, 1-23 The Bagozzi, R.P. and Fornell, C. 1982, Theoretical concepts, measurements, and meaning, in Fornell, C. (Ed.), A Second Generation of Multivariate Analysis, Vol. 1, Praeger, New York, NY, 24-38. Baraghani Sara Naimi 2007, Factors influencing the Adoption of Internet banking, Lulea University of Technology Lulea, Sweden, master thesis Bhattacherjee, Anol (2002), “Individual Trust in Online Firms: Scale Development and Initial Test,” Journal of Management Information Systems, 19(1), 211-241 Biljon J. and Kotzé P. (2008), Journal of Universal Computer Science, vol. 14, no. 16 (2008), 2650-2679 submitted: 9/7/07, accepted: 15/10/07, appeared: 28/8/08 © J.UCS McNeese William (2008) BPI Consulting accessed from http://www.spcforexcel.com/are-skewness-and-kurtosis-useful-statistics, retrieved on 07-06-2012

Brent Warrington (2008) Is Your Internet Banking Solution Costing You Customers? First Data Corporation, Retrieved from http://www.firstdata.com/downloads/thoughtleadership/fd_internetbanking_whitepaper.pdf accesed on 19/05/2012 Bryman Alan (2004) Social Research Methods 2nd Ed., Oxford University Press Bryman Alan and Burgess Robert (1999) Qualitative Research, Sage Publications Ltd London Bryman Alan and Cramer Duncan (2001) Quantitative data Analysis with SPSS Release 10 for Windows; A guide for social scientist Burns Robert (2000) Introduction to Research Method 4th Ed., Sage Publications Ltd


67

Campbell, D. T. and D. W. Fiske (1959) "Convergent and Discriminant Validation by the Multitrait- Multimethod Matrix," Psychological Bulletin (56)2, March, pp. 81-105. Chaffey, D (2002). E-Business and E-Commerce management, Prentice Hall, Pearson Education Limited London Chin W. W. (1998) Issues and Opinion in Structural Equation Modeling. MIS Quarterly 22, vii-xvi

Chuttur, M.Y. (2009), Overview of the Technology Acceptance Model: Origins, Developments and Future Directions, Indiana University, USA, Sprouts: Working Papers on Information Systems Clark R. (1997), Promises and Threats in Electronic Commerce, Retrieved from http://www.rogerclarke.com/EC/Quantum.html accessed on February 26,2012

Costello Anna B. & Jason W. Osborne (2005), Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis, Practical Assessment Research & Evaluation, Vol 10, No

Davis Fred D., Richard P. Bagozzi, Paul R. Warshaw (1989), User Acceptance of Computer Technology: A Comparison of Two Theoretical Models: Journal of Management Science, Vol. 35, No. 8 (Aug., 1989), pp. 982-1003: retrieved from http://www.jstor.org/stable/2632151, Accessed: 27/03/2012 14:07 Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology, MIS Quarterly, 13 (3) De Winter J.C.F., Dodou D., Wieringa P.A (2009) Exploratory Factor Analysis: with small sample sizes, Multivariate Behavioral Research, 44:147 - 181 Dong Cheng; Gang Liu; Cheng Qian; Yuan-Fang Song; , "Customer acceptance of internet banking: Integrating trust and quality with UTAUT model," Service Operations and Logistics,

http://www.rogerclarke.com/EC/Quantum.html%20on%20February%2026,2012


68

and Informatics, 2008. IEEE/SOLI 2008. IEEE International Conference on, vol.1, no., pp.383-388, 12-15Oct.2008 doi: 10.1109/SOLI.2008.4686425 Dulle F.W. and Minishi-Majanja M.K. The suitability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model in open access adoption studies Information Development February 2011 27: 32-45, doi: 10.1177/0266666910385375 Dimitrios Xanthidis, David Nicholas, (2004) "Evaluating internet usage and ecommerce growth in Greece", Aslib Proceedings, Vol. 56 Iss: 6, pp.356 – 366 Easterby-Smith, M., Thorpe, R., Jackson, P. and Lowe, A. (2008) Management Research (3rd edn). London: Sage Efendioglu, A.M., Yip, V.F., Murray, W.L., (2004), E-Commerce in developing countries: issues and influences, San Francisco: University of San Francisco Press. E-business in construction Chichester, UK; Ames, Iowa: Wiley-Blackwell, 2008. E-business and e-commerce management: strategy, management, and application, (2002) P: 6 Eugene juwah “A Decade of Telecom Revolution in Nigeria: The Prospects and Challenges” Retrieved from http://www.ncc.gov.ng/media-pr/speeches.html accessed on February 11, 2012 Erach N. Deboo, George L. Robb and David C. Yen, 2003 International web development: a detailed analysis by Regions, Ernst and Young 2011 A new era of customer expectation: Global Consumer Banking Survey 2011 Retrieved from http://www.ey.com/Publication/ Accessed on February 13, 2012

Fisher C. (2007) Researching and Writing a Dissertation- for business students, Prentice Hall, Pearson Education Limited. Fornell, C. and Larcher, D. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 28, 39-50


69

Foon and Fah (2010) Internet adoption in Kuala Lumpur: An Application of UTAUT Model: International journal of Business and Management; Vol 6, No. 4 Gefen D. and D. Straub “A Practical Guide to Factorial Validity Using PLS-Graph: Tutorial and Annotated Example,” Communications of the Association for Information Systems (Volume 16, 2005) 91-109 Gosling, P. Changing Money: how the Digital Age is Transforming Financial Services. Dulles, VA: Capital Books, Inc., 2000 Ghauri P., Gronhaug K., Kristianslund I., (1995). Research Methods in Business Studies: A Practical Guide 1st Ed Prentice Hall Grabner-Krauter, S. and Faullant, R., Consumer acceptance of internet banking: the influence of internet trust: International Journal of Bank Marketing Vol. 26 No. 7, 2008, pp. 483-504 Emerald Group Publishing Limited Genis-Gruber A. and Tas B.K. (2009) Cultural Differences, Information Technology Infrastructure, and E-Commerce Behavior: Implications for Developing Countries, retrieved from http://www.igi-global.com/viewtitlesample.aspx?id=10115, accessed on March 5, 2012

Habing Brian (2003) Exploratory Factor Analysis - University of South Carolina Hosmer, L.T., (1995) “Trust: The Connecting Link between Organizational Theory and Philosophical Ethics”, Academy of Management Review, Vol.20 No,2, pp.379-403.

Hutchinson Damien, Matthew Warren, (2003) "Security for Internet banking: a framework", Logistics Information Management, Vol. 16 Iss: 1, pp.64 - 73

Iacovou, C.L., Benbasat, I. and Dexter, A.S. (1995), “Electronic data interchange and small organisations: adoption and impact of technology”, MIS Quarterly, Vol. 19 No. 4, pp. 465-85. Kangis, P & Rankin, K (1996), ‘Interactive Services: How to Identify and Target the New Markets’.Journal of Marketing Practice: Applied Marketing Science, vol. 2, no. 3, pp. 44-67.


70

Khatibi A., Thyagarajan V., Seetharaman A. 2003 E-commerce in Malaysia: Perceived Benefits and Barriers, Retrieved from Kippenberger, T (2000) from intranet to extranet Retrieved from http://www.emeraldinsight.com.proxy.lib.ltu.se on February 13, 2012 Kotler, P. (2003). Marketing management, (I l I h ed.). New Dehli: Prentice Hall of India, Private Itd. Kotler P., Wong V., Saunders J., Armstrong G. Principles of Marketing Fourth European Edition published (2005), Pearson Education Limited, Essex, England P135 Kuzic Joze, Julie Fisher, Angela Scollary . (2002), electronic commerce benefits, challenges and success factors in the Australian Banking and finance industry Retrieved from http://ifiptc8.org/asp/aspecis/20020067.pdf , accessed on April 4, 2012

Laforet Sylvie & Xiaoyan Li, (2005) "Consumers’ attitudes towards online and mobile banking in China", International Journal of Bank Marketing, Vol. 23 Iss: 5, pp.362 - 380 Landis Ronald S, Daniel J. Beal and Paul E. Tesluk; A Comparison of Approaches to Forming Composite Measures in Structural Equation Models; Organizational Research Methods 2000; 3; 186; DOI: 10.1177/109442810032003 Laurie D. Jones1*, David Tucker1 and Henry Chan2 2004 E-commerce barriers in south china: The broader perspective, International Journal of Electronic Business Management, Vol. 2, No. 2, pp. 77-84 (2004) Lawrence E. Japhet and Tar Usman (2010).Barriers to ecommerce in developing countries, Information, Society and Justice, Volume 3 No. 1, January 2010: pp 23-35 ISSN 1756-1078 Lawrence, E., Corbitt, B., Tidwell, A., Fisher, J.A. & Lawrence (1998), Internet Commerce: Digital Models for Business 2nd edition, John Wiley and Sons Sydney


71

Lee Ji-Hwan, Kim Soo Wook, Song Chi Hoon (2010), The effects of trust and perceived risk on users’ acceptance of ICT services; http://www.business.kaist.ac.kr/upload/paper/KCB-WP-2010-007.pdf Lehmann (1988) An Alternative Procedure for Assessing Convergent and Discriminant Validity Applied Psychological Measurement Vol 12, No. 4, December 1988, pp. 411-423 Retrieved from http://conservancy.umn.edu/bitstream/104316/1/v12n4p411.pdf accessed on May 1, 2012. Luštšik Olga, e-banking in estonia: reasons and benefits of rapid growth, kroon & economy No 3, 2003, retrieved from /www.eestipank.ee accessed on 26/6/2012 MacGregor, R. & Vrazalic, L. (2005) The Role of Strategic Alliances in the Perception of Benefits and Disadvantages of E-Commerce Adoption in SMEs, to appear in Khosrow-Pour, M. (ed) Advanced Topics in Electronic Commerce, IDEA Group Publishing. Miria A. Pigato Information and Communication Technology, Poverty, and Development In sub-Saharan Africa and South Asia August 2001 Retrieved from http://www.worldbank.org/afr/wps/index.htm, on February 11, 2012 Mayer R.C., Davis J.H., Shoorman, F.D. (1995). An intergrative model of organizational Trust, Accademy of Management Review, Vol.20, No.3, pp. 709-734 Miles M, B and Hubberman M, A (1994) “Qualitative data analysis 2nd edition” London Sage publications Muniruddeen L. (2007). An Examination of Individual’s Perceived Security and Privacy of the Internet in Malaysia and the Influence of This on Their Intention to Use E-Commerce: Using An Extension of the Technology Acceptance Model. Journal of Internet Banking and Commerce, vol. 12, no.3

Man Zhang, Patriya S. Tansuhaj, (2007) "Organizational Culture, Information Technology Capability, and Performance: The Case of Born Global Firms", Multinational Business Review, Vol. 15 Iss: 3, pp.43 – 78

http://www.business.kaist.ac.kr/upload/paper/KCB-WP-2010-007.pdf

http://www.business.kaist.ac.kr/upload/paper/KCB-WP-2010-007.pdf


72

Michalos, Alex C. (1990) The impact of trust on business, international security and the quality of life Journal of Business Ethics (JBE), Vol. 9, No. 8 pp.619 - 638. Nitsure R.R, 2003, E-banking: Challenges and Opportunities, Economic and Political Weekly December 27, 2003 Ndukwe E.C.A. (2006), “Towards an era of unified licensing in Nigeria.” Retrieved from www.ncc.gov.ng, accessed on April 4, 2012 Nunnally, J.C. & Bernstein, I.H. (1994), Psychometric theory, New York, NY: McGraw-Hill. Ojeka, Stephen A and Ikpefan, O. Ailemen (2011), Electronic Commerce, Automation and Online Banking in Nigeria: Challenges and Benefits; Retrieved from http://www.iiuedu.eu/press/journals/sds/SDS_2011/BME_Article5.pdf, accessed on April 4, 2012 Oghenerukeybe E. A. (2009), Customers Perception of Security Indicators in Online Banking Sites in Nigeria, Journal of Internet Banking and Commerce, April 2009, vol. 14, no.1 (http://www.arraydev.com/commerce/jibc/) Ovia Frances (2007) retrieved from http://www.cipaco.org/spip.php?article1158, accessed on February 11, 2012 Ovia Jim (2001), Internet banking: practices and potentials in Nigeria Retrieved from http://www.zenithbank.com/internet_practices.pdf accessed on February 11, 2012 Oshlyansky, L., Cairns, P. and Thimbleby, H (2007) Validating the Unified Theory of Acceptance and Use of Technology (UTAUT) tool cross-culturally. British Computer Society, Volume 2 Proceedings of the 21st BCS HCI Group Conference OECD (1998), “Dismantling the Barriers to Global Electronic Commerce, Turku (Finland): 19-21 November 1997 - Conference Report”, OECD Digital Economy Papers, No. 38, OECD Publishing. http://dx.doi.org/10.1787/236647320075

http://www.iiuedu.eu/press/journals/sds/SDS_2011/BME_Article5.pdf


73

Park JungKun, SuJin Yang, Xinran Lehto(2007) Adoption of Mobile Technologies for Chinese Consumers: Journal of Electronic Commerce Research, VOL 8, NO 3

Pikkarainen, T., Pikkarainen, K., Karjaluoto, H. and Pahnila, S., (2004), “Consumer acceptance of online banking: an extension of the technology acceptance model”, Internet Research, Vol. 14 No. 3, pp. 224–235. Polite D.F (2010), Statistics and Data Analysis for Nursing Research, 2nd ed. Pearson Educational Inc. New Jersey, USA Robert C. MacGregor, Lejla Vrazalic, (2005) "A basic model of electronic commerce adoption barriers: A study of regional small businesses in Sweden and Australia", Journal of Small Business and Enterprise Development, Vol. 12 Iss: 4, pp.510 – 527 Robson, C. (2002) Real World Research (2nd edn). Oxford: Blackwell. Rogers EM., (2003) Diffusion of innovations. 5th ed. New York: Free Press; 2003.

Sadeh, Norman (2002) M-commerce: technologies, services, and business models / New York: Wiley, cop. Salthouse Timothy A. and Davis Hasker P. (2006)Organization of Cognitive Abilities and Neuropsychological Variables across the Lifespan; Developmental Review, v26 n1 p31-54 Mar 2006 Sapsford Roger and Jupp Victor (1996) Data Collection and Analysis, Sage Publications Ltd London Saunders M, Thornhill A. (2003). Research Methods for Business Students, 3rd Ed., USA Yin R,K (1994) “Case study research. Designs and Methods 2nd ed” Thousand Oaks; Sage Publications, Inc Saunders M., Lewis P. and Thornhill A. 2009, Research methods for business Students, Prentince Hall, Pearson Education Limited 5th edition


74

Sekaran Uma (2000) Research Methods for Business - A Skill Building Approach, 4th Edition Wang Ming, (2003) Assessment of E-quality via E-Satisfaction in E-Commerce Globalization; Carlifornia University, Los Angeles Sheppard, B.H.; Hartwick, J. & Warshaw, P.R (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325–343. Retrieved from www.jstor.org Accessed on February 24, 2012 Shaules, J. (2007) Deep Culture: The Hidden Challenges of Global Living. London: Multilingual Matters Ltd.

Shittu O. (2010), the impact of electronic banking in Nigeria Banking system (critical appraisal of unity bank plc), retrieved from http://upload.wikimedia.org/wikipedia/commons/d/d2, Accessed on April 4, 2012 Siriluck Rotchanakitumnuai, Mark Speece, (2003) "Barriers to Internet banking adoption: a qualitative study among corporate customers in Thailand", International Journal of Bank Marketing, Vol. 21 Iss: 6/7, pp.312 - 323 Stambro Robert and Svartbo Erik, (2002; P30) Extranet Use in Supply Chain Management. A case of three Companies. Lulea University of Technology Lulea, Sweden, master thesis Straub D. and Gefen D M. C. Boudreu (2004) “Validation Guidelines for IS Positivist Research,” Communications of the Association for Information Systems (14) pp. 380 - 426 Suhr Diana D. (2006) Exploratory or Confirmatory Factor Analysis? SUGI 31 Proceedings, Paper 200-31 Tassabehji Rana (2003) Applying Ecommerce in Business, Sage Publications Ltd, London Teo M. and Tan T. (2000) factors influencing the adoption of internet banking; Journal of the Association of Information Systems Volume 1 Article 5 July 2000


75

Timmers Paul; Electronic Commerce Strategies and models for for Business-To-Business Trading, John Wiley and Sons NewYork (1999) P4 Turban, E. and King, D. (2003). Introduction to E-Commerce. Prentice Hall, Pearson Education. Venkatesh, V. and F.D. Davis (2000). A theoretical extension of the technology acceptance model: Four longitudinal Field Studies. Management Science, Vol. 46, No 2, February 2000, 186-204. Article retrieved October 16, 2003 from PROQUEST database Venkatesh et al (2003) User acceptance of information Technology: toward a unified view MIS Quarterly Vol. 27 No. 3. pp. 425-478/September 2003 Wenninger John (2000) The Emerging Role of Banks in E-Commerce, Currentissues in economics and finance: volume 6, no. 3 Whiteley D (2000) e-Commerce: Strategy, Technologies and Applications, McGraw Hill, Maidenhead.

Wielkiewicz R. M. (2000) SPSS Online Guide McGraw-Hill Higher Education. Retrieved from http://www.mhhe.com/socscience/psychology/runyon/spss/spss.html. Accessed on May 1, 2012 Wierzbicki, Adam. Trust and Fairness in Open, Distributed Systems Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. Wu, J., & Wang, S. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729. doi: 10.1016/j.im.2004.07.001 Yin, R. K. (2003), Case study research, design and methods, 3rd ed. Newbury Park: Sage Publications


76

Yi-Shun Wang, Yu-Min Wang, Hsin-Hui Lin, Tzung-I Tang, (2003),"Determinants of user acceptance of Internet banking: an empirical study", International Journal of Service Industry Management, Vol. 14 Iss: 5 pp. 501 – 519 Zaltman G. and Barabba G.P (1991), Hearing the voice of the market: competitive advantage through creative use of marketing information Harvard Business School Press, Boston USA Zwass, V. (1998) "Structure And Macro-Level Impacts of Electronic Commerce: From Technological Infrastructure to Electronic Marketplaces" [WWW Document], URL: http://www.mhhe.com/business/mis/zwass/ecpaper.html, accessed 7 Sept 1999.

Internet Links http://www.au.capgemini.com/news-and-events/news/2011-world-retail-banking-report majority-of-banking-customers-satisfied-with-their-their-retail-banks/ accessed on June 2, 2012 http://www.referenceforbusiness.com/management/Str-Ti/Technology-Management.html accessed on February 20, 2012 www.vikalpa.com/pdf/articles/2003/2003_july_sep_77_82.pdf accessed on February 18, 2012 http://www.brainyquote.com/quotes/authors/b/bill_gates.html#ixzz1m0C6rZFX accessed February 11, 2012 http://www.internetworldstats.com/top20.htm accessed on February 11, 2012 http://www.imf.org/external/np/exr/ib/2008/053008.htm accessed on February 11, 2012 http://www.investorwords.com/2182/globalization.html accessed on February 11, 2012 http://www.ncc.gov.ng/industry-statistics/industry-data.html http://www.comscore.com/Press_Events/Press_Releases/2010/5/comScore_Releases_2010_U.S._State_of_Online_Banking_Report


77

http://www.ecominfocenter.com/newsletter/issue1/benefits.html retrieved on February 17 2012 http://www.ftc.gov/bcp/edu/pubs/consumer/credit/cre14.pdf accessed on April 4, 2012 https://www.cia.gov/library/publications/the-world-factbook/geos/ni.html accessed on April 4, 2012 http://www.rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/21595.pdf


78

Appendix: A Listed Banks in Nigeria and their e-service channels

BANK

CHANNELS

SOURCE I-BANKING ATM M-BANKING

1 Access Bank Nigeria www.accessbankplc.com

2 Diamond Bank partially www.diamondbank.com

3 Ecobank Nigeria partially www.ecobank.com

4 Enterprise Bank NA NA NA NA

5 Fidelity Bank www.fidelitybankplc.com

6 FinBank www.finbakplc.com

7 First Bank Nigeria partially www.firstbanknigeria.com

8 First City Monumental Bank www.fcmb-ltd.com

9 Guaranty Trust Bank www.gtbplc.com

10 Keystone Bank Ltd www.keystonebankng.com

11 Mainstreet Bank Limited NA NA NA www.mainstreetbanklimited.com

12 Nigeria International Bank NA NA NA www.citigroup.com/global/nga

13 Skye Bank Nigeria Ltd. partially www.skyebankng.com

14 Stanbic IBTC Bank Ltd. NA NA NA www.ibtc.com

15 Standard Chartered Bank Not listed www.standardchartered.com/ng

16 Sterling Bank Nigeria www.sterlingbankng.com

17 Union Bank Of Nigeria Not listed www.unionbankng.com


79

18 Unity Bank Nigeria www.unitybankng.com

19 United Bank For Africa www.ubaplc.com

20 Wema Bank www.wemabank.com

21 Zenith Bank Plc www.zenithbank.com

Appendix: B Survey Instrument Indicator Description Type NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA PE1 PE2 PE3 PE4

Biographical Information In what Country do you reside What is your nationality Gender Marital Status Age Highest level of Education Type of employment How long have you been using the internet How often do you use internet per day In General how often do you use the internet Do you use the Internet for banking tasks in Nigeria For what tasks do you use internet banking How would you describe your internet knowledge How would you describe your general computer knowledge

UTAUT Measurements Performance Expectancy Using Internet banking services enables me to accomplish banking tasks more quickly Using Internet banking services will increase my effectiveness when carrying out banking tasks Using Internet banking will improve the effective use of my time. I find using Internet banking useful for carrying out banking tasks

Effort Expectancy

Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable Dummy Variable 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale


80

EE1 EE2 EE3 EE4 SI1 SI2 SI3 SI4

FC1 FC2 FC3 FC4 TR1 TR2 TR3 TR4 BI1 BI2 BI3

Learning to use the Internet banking services is easy for me I would find Internet banking services easy to use My interaction with Internet banking would be clear and understandable It would be easy for me to become skillful at using Internet banking services

Social Influence People who influence my behavior think I should use Internet banking services. People who are important to me think that I should use Internet banking facilities The bank staffs are helpful in the use of Internet banking services. The bank branch encourages use of Internet banking services

Facilitating Condition I have knowledge necessary to use Internet banking services I have access to computer and Internet. I think that Internet banking fits well with the way I like to work. All the contents of Internet banking service are easy to read and understand.

Trust The bank is competent in providing Internet banking services. Internet banking site is interested in my well-being, not just its own. I would characterize Internet banking site as honest. Internet banking has enough specialists to detect fraud and information theft.

Behavioral Intention I intend to use Internet banking service in the near future. I predict I would use Internet banking service in the near future. I plan to use Internet banking service in a near future

1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale’ 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale 1-5 Likert Scale

Appendix: C Acronyms TAM: Technology Acceptance Model I-Banking: Internet Banking TRA: Theory of Reasoned Action UTAUT: Unified Theory of Acceptance and Use of Technology IDT: Innovation Diffusion Theory DOI: Diffusion of Innovation BI: Behavioral Intentions


81

PC: Personal Computer E-Banking: Electronic Banking ICT: Information and Communication Technology GDP Gross Domestic Product ATM: Automated Teller Machine. EDI: Electronic Data Interchange EDP: Electronic Data Processing IT: Information technology SCT: Social Cognitive Theory MM: Motivational Model MPCU: Model of PC Utilization TAM2: Technology Acceptance Model 2 TPB: Theory of Planned Behavior C-TAM-TPB: Combined TAM and TPB

Appendix: D Cross Correlations Matrix Correlations

BI PE EE SI FC TR

Pearson Correlation

BI 1.000 .493 .432 .262 .493 .316

PE .493 1.000 .490 .169 .632 .285

EE .432 .490 1.000 .210 .721 .196

SI

FC

TR

.262

.493

.316

.169

.632

.285

.210

.721

.196

1.000

.312

.528

.312

1.000

.513

.528

.513

1.000

Sig. (1-tailed)

BI . .001 .003 .051 .001 .023

PE .001 . .001 .148 .000 .038

EE .003 .001 . .097 .000 .113

SI .051 .148 .097 . .025 .000

FC .001 .000 .000 .025 . .000

TR .023 .038 .113 .000 .000 .


82

APPENDIX: E Histogram Showing Skewness


83

List of Figures: Figure: 1 - Title: Telecommunications Subscriber data 2000 – 2010 Figure: 2 - Title: Thesis Disposition Figure: 3 - Title: Theory of Reasoned Action Figure: 4 - Title: Technology Acceptance Model Figure: 5 - Title: Unified theory of acceptance and use of technology Figure: 6 - Title: Model of trust Figure: 7 - Title: Conceptual Framework

List of Tables: Table: 1 - Title: Top 20 Countries with the highest number of internet users Table: 2 - Title: Summary of e-commerce adoption barriers in small businesses Table: 3 - Title: Models and Theories of Individual Acceptance. Table: 4 - Title: Summary of some previous researches done on internet banking Table: 5 - Title: Operationalization of Performance Expectancy Table: 6 - Title: Operationalization of Effort Expectancy Table: 7 - Title: Operationalization of Social Influence Table: 8 - Title: Operationalization of Facilitating Conditions Table: 9 - Title: Operationalization of Trust Table: 10 - Title: Operationalization of Behavioral intentions Table: 11 - Title: Comparison of the Quantitative and Qualitative Research Methods Table: 12 - Title: Comparative Analysis between Techniques. Table: 13 - Title: Frequencies and Percentages Table: 14 - Chi-Square Tests for Internet Knowledge and the effect on Internet banking usage Table: 15 - Chi-Square Tests for Employment and the effect on Internet banking. Table: 16 - Chi Square test of Gender and the effect on Internet Banking Usage Table: 17 - Chi Square test of Computer Knowledge and the effect on Internet Banking Usage Table: 18 - First batch of Factor Analysis Table: 19 - Second Batch of Factor Analysis Table: 20 - Descriptive Statistics of Items included in the Model Table: 21 - Cronbach’s Analysis for Reliability Table: 22 - Model Summary of Multiple Regressions Analysis Table: 23 - Multiple Regressions Coefficient Table: 24 - ANOVA calculations for – PE


84

Table: 25 - ANOVA calculations for – EE Table: 26 - ANOVA calculations for – SI Table: 27 - ANOVA calculations for – FC Table: 28 - ANOVA calculations for – TR Table: 29 - Beta Weight Calculations for the Simple Regression Test Table: 30 - Summary of the Test of Hypothesis

behavoral intention to adopt internet banking1018195/fulltext02.pdf · bundle and it is an...

Documents