University of Ghana http://ugspace.ug.edu.gh STATISTICAL ANALYSIS OF FACTORS INFLUENCING THE ADOPTION OF INTERNET BANKING IN GHANA: THE CUSTOMER PERSPECTIVE BY UZERU ALIDU (10550433) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF THE MPHIL STATISTICS DEGREE JULY, 2017 University of Ghana http://ugspace.ug.edu.gh DECLARATION Candidate’s Declaration I, Uzeru Alidu hereby declare that apart from references to other people’s publications, which have been duly acknowledged, this thesis is a result of my independent ideas, thought, deliberations and has not been submitted for the award of any degree at this institution and other universities elsewhere. SIGNATURE: …………………………………… DATE: ……………… UZERU ALIDU (10550433) Supervisors’ Declaration We hereby certify that this thesis was prepared from the candidate’s own work and supervised in accordance with guidelines on supervision of thesis laid down by the University of Ghana. SIGNATURE: …………………………………… DATE: ……………… DR. ANANI LOTSI (PRINCIPAL SUPERVISOR) SIGNATURE: …………………………………… DATE: ……………… DR. E. N. N. NORTEY (CO-SUPERVISOR) i University of Ghana http://ugspace.ug.edu.gh ABSTRACT “Internet banking” is a product offered by commercial banks to enable clients carry out banking transaction at their convenience and at any time. It enables banks to reduce operational cost by reducing the cost of stationary and also number of staff. However, clients of commercial banks in Ghana still prefer conducting transaction at the branches of the banks rather than signing on to internet banking. This is evident in the queues by clients in commercial banks in order to carry out transaction. This study sought to find the factors influencing the adoption of “internet banking”. The study employed “Factor Anlysis” using the “Principal Axis” method of factoring and direct oblimin rotation. A chi-square test of association was performed to determine whether a relationship existed between the demographic factors and adoption of internet banking. Also, the binary logistic regression was used to determine the chance of customer adopting “internet banking” given a factor. A modification to the F statistic, the F ratio with 1000 permutation was used to compare the adoption of “internet banking” local and foreign owned banks. The study found that Trustworthiness, Usefulness, Risk, Accessibility, Ease of use, Assurance in the banks website, Service Visibility, Awareness of benefits of internet banking and Trust in Internet banking influenced the adoption of “internet banking”. Also, banks with “easy to use” platforms were 3.546 more likely to influence clients to adopt “internet banking” controlling for all other factors. There was no difference between the adoption of internet banking for locally owned and foreign owned banks ii University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENT My first acknowledgment goes to Allah for His guidance and sustenance. My Special thanks go to Prosper Kojo Amewu, Head, Compliance Department of Bank of Africa and Laureen Yirerong for their continuous support during the course of the programme. I would also like to acknowledge my supervisors Dr. Anani Lotsi and Dr. E. N N. Nortey for their timeless effort they dedicated in looking through this work. My special appreciation also goes to Dr. K. Doku-Amponsah, the Head of Statistics Department and Dr. Louis Aseidu. Finally, a special mention of all colleague MPHIL Statistics students of our year group especially Emmanuel Aidoo, Joseph Amachie and Isaac Kojo Appiah. iii University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS DECLARATION ...................................................................................................................... i ABSTRACT ............................................................................................................................ ii ACKNOWLEDGEMENT ..................................................................................................... iii TABLE OF CONTENTS ....................................................................................................... iv LIST OF FIGURES ................................................................................................................ vi LIST OF TABLES ................................................................................................................ vii CHAPTER ONE ...................................................................................................................... 1 INTRODUCTION ................................................................................................................... 1 1.1 Introduction ................................................................................................................... 1 1.2 Statement of Problem .................................................................................................... 3 1.3 Objectives of the Study ................................................................................................. 6 1.4 Significance of the Study ............................................................................................... 6 1.5 Research Methodology .................................................................................................. 7 1.6 Organization of the Thesis ............................................................................................. 8 CHAPTER TWO ..................................................................................................................... 9 LITERATURE REVIEW ........................................................................................................ 9 2.1 Introduction ................................................................................................................... 9 2.2 Empirical Literature Review ......................................................................................... 9 CHAPTER THREE ............................................................................................................... 28 RESEARCH METHODOLOGY .......................................................................................... 28 3.1 Introduction ................................................................................................................. 28 3.2 Research Design .......................................................................................................... 28 3.3 Population .................................................................................................................... 29 3.4 Sampling Technique and Sample Size ........................................................................ 30 3.5 Estimation of Sample Size ........................................................................................... 32 3.6 Data Collection ............................................................................................................ 35 3.6.1 Source of Data ...................................................................................................... 35 3.6.2 Validity and Reliability of Research Instrument .................................................. 36 3.7 Method of Data Presentation and Analysis ................................................................. 36 3.7.1 Factor Analysis ..................................................................................................... 37 3.7.2 Components of Variance in Factor Analysis ........................................................ 38 3.7.3 Factor Extraction .................................................................................................. 42 3.8 The Logistic Regression Model ................................................................................... 53 3.8.1 Parameter estimation ............................................................................................ 55 3.9 Non-Parametric Multivariate Analysis of Variance .................................................... 57 iv University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR ................................................................................................................. 60 ANALYSIS AND RESULTS ............................................................................................... 60 4.1 Introduction ................................................................................................................. 60 4.1.1 Characteristics of sample ...................................................................................... 61 4.2 Factors Influencing the Adoption of Internet Banking ................................................ 65 4.2.1 Correlation Matrix ................................................................................................ 66 4.2.2 Number of Factors to Retain ................................................................................ 67 4.2.3 Grouping of Components into Factors ................................................................. 69 4.2.4 Reliability Analysis of the Grouped Factors ........................................................ 69 4.3 Chance of a Customer Adopting Internet Banking Given a Particular Factor ............ 72 4.4 Internet Banking Adoption of Locally and Foreign Owned Banks ............................. 76 CHAPTER FIVE ................................................................................................................... 79 CONCLUSION AND RECOMMENDATION .................................................................... 79 5.1 Introduction ................................................................................................................. 79 5.2 Summary ...................................................................................................................... 79 5.3 Conclusion ................................................................................................................... 81 5.4 Recommendations ....................................................................................................... 81 5.5 Limitation of the study ................................................................................................ 82 REFERENCES ...................................................................................................................... 84 APPENDICES ....................................................................................................................... 88 Appendix A: Questionnaire ............................................................................................... 88 Apendix B: Total Variance Explained .............................................................................. 93 Appendix C: Correlation Matrix ....................................................................................... 95 Appendix D List of Banks ............................................................................................... 106 v University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Figure 3.1 Sampling Procedure ............................................................................................. 30 Figure 4.1: Scree Plot ............................................................................................................ 68 vi University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Table 4.1: Gender Distribution .............................................................................................. 61 Table 4.2: Age Distribution ................................................................................................... 61 Table 4.3: Educational Level ................................................................................................. 62 Table 4.4: Occupation ........................................................................................................... 62 Table 4.5: Usage of Internet Banking .................................................................................... 63 Table 4.9: KMO and Bartlett's Test ....................................................................................... 66 Table 4.11 Reliability Statistics ............................................................................................. 69 Table 4.12: Table Total Variance Explained ......................................................................... 70 Table 4.13: Test of Association ............................................................................................. 71 Table 4.14: Hosmer and Lemeshow Test .............................................................................. 73 Table 4.15: Variables in the Equation ................................................................................... 74 Table 4.16: Multivariate Test for Normality ......................................................................... 77 Table 4.17: Test of average adoption for Local and Foreign Owned Banks ......................... 77 vii University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.1 Introduction Internet Banking according to (Kimet, 2011) is a term broadly used to describe the various banking products and services that make use of digital, internet and mobile technology. Internet banking started as telephone banking first time in 1980s and its usage ascended when internet was provided at homes of individual. In some parts of Europe and United States of America, banks and finance companies begun to work on the idea of the “home banking”. Computer and internet were not readily available as at then, hence, it was focused to the telephone banking. The application was first started in United States of America in 1996 and then later, the renowned banks such as Wells Fargo and Citibank begun to provide this facility to their client in 2001. With the aim of refining the value of service being provided to the client of commercial banks in Ghana, Banking has undergone a lot of changes in service delivery. In the past, Banks were providing services to their clients through the manual system, which results in long queues in transacting business in the banks. Additionally, companies and individuals in Ghana face the problem of clients not accepting cheques as a payment method. Hinson (2005) attribute this to the time and the problems involved in receiving and lodging cheques into the bank accounts of companies. Additionally, Hinson (2005)contends that internet enables communications on co-operative basis with one or several people, unconstrained by space or time, in a multimedia atmosphere with sound, image, text transmission, and at fairly low and declining costs. Commercials Banks with Internet banking service now aid their clients to use Internet for their banking 1 University of Ghana http://ugspace.ug.edu.gh needs. Clients are able to access their account in order to see their account balances, transfer funds, pay bills, manage their accounts as well as perform a lot of functions including accessing and printing of statements of accounts. The banking industry has advanced from old-fashioned “brick and mortar” to “internet banking”. “Internet banking” makes use of the Internet as a remote distribution mode for carrying out banking services according to Clemons and Hitt (2000). This includes Checking Balances, verifying and viewing transactions on account, printing statements, monitoring uncredited and unpaid cheques. Yang and Peterson (2004) in their studies also found that banks are able to save 107 times of the total of its cost when “internet banking” services were engaged. Pennathur (2001) found that “Internet banking” increase operational, legal, reputation risks, and increase competition therefore it promotes enhanced services amongst contending banks. “Internet banking” also allow clients to interrelate more than before with the front office of the bank and, at the same time allow banks to consolidate back office operations and grow their competence. It is the day and night availability that makes it so convenient for the bank clients. Govender and Wu (2013), states that, internet banking enables better management of funds. 58.8% of respondents strongly agreed that internet banking helped them better manage their funds. He further states that, internet banking saves time since 100% of users agreed that internet banking saves time. His results indicated that internet banking makes communication between clients easier. Like other third world countries, Ghana is not yet at par with Western countries and hence it is not expected to have identical levels of banking services. The need to elevate services to an internationally known degree has prompted some Ghanaian banks to offer “Internet 2 University of Ghana http://ugspace.ug.edu.gh banking” services. The face of banking in Ghana is fast improving and focus is now on new supply channels. This will go a long way to advance customer service and give way to 24 hours a day access to banking services. Customers are supposed to have access to their accounts and carry out transactions from the luxury of their homes and offices which hitherto would have been done in the banking halls once they have internet banking access. Using a Personal computer with an internet connection, customers can transact business on their traditional accounts such as, settlement of utility bills, cash withdrawals, accessing and printing of statements, Transfers from one account to the other, request for cheque books etc. “Internet banking” is a service where access to account information and any transactions is allowed at any time from any computer with an Internet connection. The number of people using Internet in Ukraine has been increasing by 20-30% yearly over the last 5 years. Hence, online banking becomes more normal for Ukrainian clients, and banks are encouraged to propose a new and convenient way to use their services. Customers are happy to decrease transaction costs, while banks may charge the same or even more fees. Moreover, the details of clients transactions can be readily gotten, which makes banking institutions to assess customers’ needs. Online services are likely to be the future of the banking system, and the number of “Internet banking” users is likely to continue to increase. If their behavior differs from the standard customers' one, for banks it would be particularly interesting to know how. Thus, research results would thus also be relevant for business. 1.2 Statement of Problem In the quest to make banking more convenient and also eliminate long wearisome queues in banking halls, “internet banking” is one of the recent developments that have added a new 3 University of Ghana http://ugspace.ug.edu.gh measurement to banking transactions. Humphrey, Mansell, Paré, and Schmitz (2004) contend that the internet is becoming an important business tool. Clients and companies are increasing employing the use of internet and their business and work in other to simplify task and gain readily gain access to information. There are however factors that make clients not get the benefit of adopting “internet banking”. These factors are resource constraints, ability to utilize the technology and management attitude. Lack of adequate information technology infrastructure is a critical barrier in supporting the continual growth of online commerce, according to Chircu and Kauffman (2000). Small companies in Ghana can have better plans in place to make use of the internet to increase productivity and reduce cost Hinson (2005). Again, lack of trust in the web restricts the opportunity in technology. Min and Galle (1999), (Lee & Turban, 2001) also notes that clients do not trust internet technology for three reasons. These are Lack of trust in service provider, reliability in the internet service and security of the system. Concern about security is one common factor related to unwillingness to use Internet channel for commerce. Security breaches can lead to numerous problems such as destruction of operating systems, or disruption of information access Min and Galle (1999). The use of the Internet in delivering banking services is pervasive in western developed contexts. Internet banking is however just beginning to blossom in West Africa, and Ghana in particular. Nonetheless, there are some factors which do not encourage banking through the internet and causes many customers to be physically present in the bank premises instead of taking advantage of internet banking. Low broadband internet penetration, customers' preference for traditional branches, fear of online threats/scams, lack of basic knowledge of computers and 4 University of Ghana http://ugspace.ug.edu.gh the high cost of internet accessibility are some of the problems that may threaten the growth of internet banking in Ghana. Moreover, there are certain factors which fuel this new additional way of doing business. Viola and Jones (2004) in his study found that banks save 107 times of total cost when internet banking is employed. Banks are able reduce operational cost such us stationary, number of staff, electricity and water bills and so on. Internet banking users do not need to make their way to banking halls, fill deposit slips and join queue in other to carry out a transaction. Additionally, Drigă and Isac (2014) indicates that internet banking bring sustainable competitive advantage. His results, based on world retail banking report 2014 for eight European countries, US and Japan, indicates that the internet banking user paid for transactions 34% less than an active branch user. The active branch user pays higher tariffs than an internet user. He also spends time making his way to the banking hall and staying in queues to be served. The internet banking user also has access to his account and can carry out transactions at his own convenient anytime and anywhere provided he has access to the internet. Commercial Banks in Ghana introduce internet banking in other to enable its clients carry out banking transaction at their own convenience without making their way to the branch and also at any time. Additional banks are able to reduce operational cost of business once clients adopt internet banking because of the cost of printing deposit slips other stationary for clients who carry out branch banking. Also, the number of staff employed to serve clients in tradition branch banking will be greatly reduced if clients roll on to internet banking. Additionally, with clients adopting internet banking, the rate of the country depending on cash transaction 5 University of Ghana http://ugspace.ug.edu.gh will be reduced and help the countries drive for cash light country thereby making payment and settlement easier. However, the internet banking has seen low patronage by clients of commercial banks in Ghana. Clients still prefer to go to the branches of commercial banks to carry out transactions. This study therefore seeks to examine the key factors that affect the adoption of internet banking in Ghana, using some selected international and local owned banks. The study will examine the factors that impact the adoption of internet banking in Ghana using Factor Analysis and the logistic regression. The study will also compare the study will also compare the results of locally owned and foreign Banks. 1.3 Objectives of the Study The main objective of the study is to examine the key factors which affect adoption of internet banking in Ghana, with a sample of local owned banks and foreign owned banks as case study. The following specific objectives will be accomplished. 1. To examine the factors that significantly contributes to customer’s adoption of internet banking. 2. To determine the chance of a customer’s adoption of internet banking given some significant factors 3. To compare internet banking adoption between international banks and local banks owned in Ghana 1.4 Significance of the Study Internet banking provides clients of commercial banks the opportunity to carry out banking transactions at their convenience. The study seeks to determine the factors that influence the choice of internet banking facility by clients of banks in Ghana. This will assist banks in 6 University of Ghana http://ugspace.ug.edu.gh Ghana improve on their services thereby enabling clients to enrol on the internet banking platform. Additionally, increased enrolment of clients of commercial banks on the internet banking platforms will help reduce the rate at which transactions in Ghana are cash dependant. The internet banking model enables bills and payments to be effected easily without the use of cash. Utility bills can be paid using the internet banking facility without having to withdraw money from your account and making payments at the offices of the utility service. Therefore the study will enable the government identify the factors that determines the choice of internet banking so as to formulate policies that will geared towards making Ghana a cashless society. 1.5 Research Methodology This research will begin with a detailed literature review. Then, a qualitative approach will be employed in order to design a questionnaire, after which a quantitative approach will be used in order to collect data and test theories. Semi-structured interviews help to “build a complex, holistic picture, formed with words, reporting detailed views of informants and conducted in a natural setting” Creswell et al. (1994). The theoretical review and qualitative study provide the foundations for measurement items and constructs included in the integrated models. The quantitative research helps to test the theories “composed of variables, measured with numbers, and analysed with statistical procedures” Creswell et al. (1994). This research employs a predominantly quantitative approach in order to test the hypotheses and to help us understand the related phenomena on internet banking adoption in Ghana by the examining the relationships between various factors and the adoption of internet in Ghana, as well as comparing the differences between adopters and non-adopters. 7 University of Ghana http://ugspace.ug.edu.gh 1.6 Organization of the Thesis Chapter one, which is the introduction, entails the background of the study, the problem statement, the objectives, the significance of the study, the research methodology and the organization of the study. In Chapter two, that is the literature review; related previous researches and literature on internet banking adoption are obtained and reviewed to enrich this study. Chapter three has the heading methodology, which deals with the sampling technique and sample size, data collection, data management, reliability and validity of the instrument, and also treated factor analysis, Binary logistic regression analysis and a modification of the F statistic, F-ratio test with permutation for comparison of the location parameter (median) in the non-parametric analysis of variances. Further, Chapter four deals with the presentation of results emanating from the analysis of the data using charts, frequency tables, factor analysis, Binary logistic models and the F-ratio test. It also contains the discussion of the study. Finally, Chapter five comprises a summary of the findings, conclusions and some suggested recommendations. 8 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO LITERATURE REVIEW 2.1 Introduction The chapter seeks to review existing literature that discusses the main conceptual pillars relevant to the study. It identifies what research has been conducted to date, and where the current study stands in relation to that research. The main domain of the literature studied relates to behavioural intention toward Internet Banking adoption. Furthermore, five theoretical models related to behavioural intention toward Internet Banking adoption will be discussed and then combined, in order to form the theoretical foundation of this study. The first part of this chapter examines relevant theories, and reports on the empirical applications of these theories. The second section goes on to review the general literature on Internet Banking, in order to identify gaps in the research. 2.2 Empirical Literature Review In the area of adoption of new technologies, several researches have been carried out and more specifically on adoption of internet banking. These studies can generally be classified into three groups. Conditions that are necessary for bank customers to start using Internet banking is the first group investigated. Based on surveys, customers tend not to use the service according to Al- Al-Rfou, Perozzi, and Skiena (2013) even if the service is provided. The complex nature of its usage, privacy being low and poor quality of Internet connection are the proposed reasons for clients not subscribing to Internet Banking in Jordan according to Al-Rfou et al. (2013). His study considered clients of commercial banks in Jordan. Questionnaires were 9 University of Ghana http://ugspace.ug.edu.gh administered to clients and data gathered were analysed using Simple Regression Analysis. The study revealed that the usage of internet banking in Jordan was weak. It also was discovered that there is a strong association amongst the use of internet banking, security and privacy, ease of use, and quality of internet connection. There are many gaps identified in the study. Firstly, the study did not use the appropriate model (Simple Regression Model). This is because, the dependent variable (Use of internet banking) was measured in likert scale hence it was not a continuous variable and does not meet the assumptions in other simple linear regression. Secondly, the regression analysis was done using only one independent variable at a time. For example, regression was done on ‘internet banking usage’ and ‘ease of usage’ without reference to the combined effect of all the other independent variables such us, ‘security and privacy’, ‘quality of internet connections’ etc. Also, the research did not consider the effect of other important variables like, the ‘Risk’, ‘usefulness’ and ‘the service reliability’ of internet banking adoption. To breach these gaps, the appropriate model which is Logistic regression coupled with Factor Analysis were used. Factor Analysis was used to obtain the significant factor that contributes to the adopting internet banking in Ghana and since the dependent variable is categorical with several levels, Logistic Regression was used to find the chance of a customer adopting internet banking given some significant factor. Al-Rfou et al. (2013) also confirmed this evidence in his study. Awareness was added as an important factor. Questionnaires were distributed among users of internet banking from 10 University of Ghana http://ugspace.ug.edu.gh different bank. The study showed that the demographic characteristics of customers also affect the usage of internet banking as well. Karacaoglu, Bayrakdaroglu, and San (2012) in his study on developing internet banking as an innovative distribution channel in Turkey example also notes that consumers making use of internet banking products have increased over the time. This has overcome the clash between banking industry security, web infrastructure and diversifying banking products. Hence the tendency of the use of web banking was strengthened and effectively, sped-up development of internet banking. Karacaoglu et al. (2012) also concludes that to heighten the use of internet banking in Turkey, initially, you need to investigate attitude users in detail in other know them better. Due to increasing demand for internet banking, banks do not open new branches and operational cost staff is reduced. It therefore reduces cost generally and makes services fruitful and effective. Usage of internet banking in the coming years is anticipated to grow and make banks to provide creative and innovative solutions. When compared with other countries in the world, Ali contends that Turkey is not at the required level in the use of internet banking. He noted that banks should consider it more important to take some precautions when factoring in this position, so as to spread electronic services. Security problem must be solved considering each technological system. It is therefore important that banks provide fast and effective communication to customers in the instance that there are uncommon movements on the accounts of customers. Ali also stated that, in Turkey there is an increasing trend in internet usage although it has less internet usage ratios as compared to developed countries. 11 University of Ghana http://ugspace.ug.edu.gh His research used descriptive statistics using mean and frequency approach. He also used trend analysis in analysing the active number of internet banking users. His study concentrated on the increase in internet banking adoption in Turkey rather than what accounted for the rise of internet banking adoption in Turkey. The perspective of the customer was not examined. Koskosas (2008), emphasise the importance of trust and stringent security control for efficient Internet banking. His study examined the advantages and disadvantages of internet banking adoption. The study concludes that has increased the completion among banks and also provides convenience and save time. He also added that people have the difficulty of fearing possibility of identity theft and the security of online transactions. Some banks take the risk of identity theft more seriously than others even though it is of significant concern. In other for clients to ensure that their expectations are met, they have to investigate the banks security policies and protection before enrolling on the internet banking platform. A customer may adopt internet banking of a bank because of the trust in that bank. His study only looked at the benefits and the disadvantages relating to internet banking adoption as opposed to this study which looks at the elements affecting internet banking adoption using Factor Analysis and Logistic regression. Also, Yee-Loong Chong, Ooi, Lin, and Tan (2010) in their research on growing the Chinese Internet Banking Sector noted that trust is a key predictor of positive word of mouth and generates more explanatory power than perceived risk, which reduces word of mouth of mouth intention. Perceived justice is mediated by trust and leads to positive word of mouth recommendation. Interestingly, perceived industry reputation has a stronger positive influence on perceived justice than on trust; a positive industry reputation also reduces 12 University of Ghana http://ugspace.ug.edu.gh Chinese consumers’ risk perception. They also noted that attention must be given to resolve consumers’ low perceived justice perception, low trust in the bank and its Internet banking system in order to minimize unpleasant experience and negative word of mouth. Their study developed an online survey using quantitative design. Questionnaire was administered online to respondents in other to get data for the study. They also adopted convenient sampling technique in carrying out the study. Their study also adopted Factor Analysis in ascertaining factors that accounted for the growth of internet banking adoption in china. This study will administer questionnaire to the various customers of the banks. Commercial banks in Ghana have been grouped into two categories; locally and foreign owned banks. In contrast to their study, multistage sampling will be employed to get sample for the study. Also, in addition to the Factor Analysis, Logistic Regression will be employed to determine the chance of a customer adopting internet banking given a significant factor. Also, Thulani and Tofara in thier study of internet adoption and use in Zimbabwe, noted that even though there is a good adoption rate of internet banking in Zimbabwe, the usage of the service has remained relatively low since a lot of consumers are not using the facility many in the country. The implication he noted was that, banks in Zimbabwe needed to enhance their efforts at marketing by making customers aware and getting them interested in internet banking. The major use of internet banking in Zimbabwe, Tofara observed, was for balance checks, bill payments and funds transfer. (Thulani & Tofara) also found that the perceived merits of internet banking use in Zimbabwe were a reduction in cost, loyalty increase and to attract new customers. He however noted that in the process of adopting internet banking by banks in Zimbabwe there were several 13 University of Ghana http://ugspace.ug.edu.gh challenges such as cost of implementation, legacy systems, and security concerns compatibility among others. He therefore suggested that the Zimbabwean Government should, increase investments in infrastructure development and education through the Reserve Bank of Zimbabwe, to enable more consumers and firms to adopt internet Banking. (Thulani & Tofara)considered all commercial banks in Zimbabwe in carrying his study. To this end questionnaires were distributed to all the commercial banks in Zimbabwe. Out of the sixteen (16) commercial banks, twelve (12) banks responded to the questionnaire. Descriptive statistics and simple linear regression analysis was used in analysing the data. His methods of getting data for the study did not consider the clients of the commercial banks but was rather limited to the banks in Zimbabwe. This did not allow for the research to determine appropriately what accounted for the adoption of internet banking by clients of commercial banks in Zimbabwe. The method only could only identify what commercial banks of Zimbabwe thought were the reasons why clients adopted internet banking. To bridge this gap, this study will solicit response of clients of commercial banks instead of the commercial banks. Additionally, the use of descriptive statistics and linear regression analysis for in analysing the data was not appropriate since the dependent variable is categorical with several levels, Logistic Regression will be used to determine the chance of a customer adopting internet banking given some significant factor. The second group of research assesses the aggregate effect of banking performance with respect to internet banking. Drigă and Isac (2014) claim that, Internet banking can result in commercial banks gaining a competitive edge in terms of shares on the market, but not in commercial banks profit making. The results are based on a report on the World Retail 14 University of Ghana http://ugspace.ug.edu.gh Banking 2009 (for 8 European countries, the US and Japan). It reveals that a client who is active in the use of internet banking paid for transactions of 34% lesser than a client who uses the branch on average. However, they noted that these findings were influenced by the policy of European banks' to aggressive discourage customers from visiting branches. They also found that commercial banks were able to assist and serve customers better using new technologies anywhere the customer is located in the world not necessarily in bank branches. Also, customers are visiting branches of commercial banks less often and they use internet and mobile technology for their banking needs more often as a result of the convenience digital platforms provide. There is a fast growth in online and mobile banking while branch importance is declining quickly. When it came to getting banking advice, he noted that clients of commercial banks still preferred branch banking. He further stated that mobile and internet banking have become the main medium for clients to interact with commercial banks even though they do not completely replace the other channels. Hence, in the nearest future internet banking will overcome traditional banking. Their study used trend analysis in the analysis of their results and also concentrated on the data on internet banking adoption globally. They did not take the customer into perspective as opposed to this study. (Bouckaert and Degryse (1995)argue about two opposite effects of remote banking services on interest rates. Firstly, they promote depositors to add more saving accounts or keep more funds on existing ones, which facilitates attraction additional deposits at current interest rates. Secondly, providing remote services can decrease customer's transaction costs for other banks that offer similar services, facilitating competition and causing increase in interest rates. Their study also reveals that clients of commercial banks who adopt internet banking are different 15 University of Ghana http://ugspace.ug.edu.gh from others on characteristics such as wealth, income, age and activity before the start of usage. Their study considered account balances of clients and the number of transactions on clients account as the variable of interest. They considered the factors that push customers to adopt internet banking as endogenous and not observable but do not fluctuate. Panel data regression was therefore used with individual fixed effects before and after internet banking adoption which eliminates sample selection bias. This study however looks at responses from clients concerning factors that accounts for the adoption of internet banking. Also, their study only considered data from one Bank in Ukraine. The selected customers were monitored over a period. This approach could lead to the selection of bias samples for the study as problems associated with that particular bank will affect results of the study. This study however considers samples from different banks in Ghana hence reduce bias in the samples. In the third group the levels of satisfaction and loyalty of clients of commercial banks with Internet banking are measured. Fathima and Muthumani (2015), worked on how satisfaction of customers can be influenced by the quality of online services provided. They noted that seven factors were found to impact customer loyalty in internet banking of a bank. These factors are, ‘Customer Satisfaction’, ‘Service Quality’, ‘Service Value’, ‘Brand Reputation’, ‘Trust’, ‘Habit’, and ‘Switching Cost’. Of these factors, four was found to be predominant in influencing bank customer loyalty. These are, Customer Satisfaction, Trust, Habit and reputation of the bank. Their study in gathering the data used convenient sampling technique to collect data for study. The questionnaire they administered was in a likert scale form of measurement. Also, their 16 University of Ghana http://ugspace.ug.edu.gh study employed multiple Regression analysis in other to ascertain the relationship between customer loyalty in internet banking and the construct. Their study did not use the appropriate model (Multiple Regression Analysis Model).This is because, the dependent variable (Use of internet banking) was measured in likert scale hence it was not a continuous variable and does not meet the assumptions in other multiple regression analysis. Also, the use of convenient sampling technique could have led to choosing bias sample for the study thereby rendering the results to representative of the population. To bridge these gabs, this study employed the multistage sampling technique in other to get a more representative set of data. Additional, this study will employ Logistic Regression analysis and the Factor Analysis models since they meet the assumptions for use in the case of likert scale measurement. Also, Gulati et al. (2013)studied how a client is influenced by quality of internet services. They assessed internet banking and customer satisfaction in Pakistan. Their study showed a positive relationship existing between tangibility, assurance, responsiveness and reliability with customer satisfaction and internet banking usage in Pakistan. They suggested that, in other to get the attention of both existing and new clients, management of commercial banks offering internet banking have to focus on making the content and design of the websites more appealing. They further have to enhance the safety and security of internet banking platforms so that clients of their banks can maintain longer with the bank. Commercial banks providing the internet banking service need to provide a more reliable service to their clients to make the customers more confident and comfortable in the bank. Effective systems should be developed by management of banks to solve the issues raised by customers quickly. 17 University of Ghana http://ugspace.ug.edu.gh They also used factor analysis and multiple regression analysis to establish the link between the two variables: internet banking and customer satisfaction. Pikkarainen, Pikkarainen, Karjaluoto, and Pahnila (2004) also found that two reasons fundamentally accounted for the diffusion and development of internet banking. First, there is reduction in cost incurred by banks as a result of internet banking services provided. Research by Sathye (1999), shows that once put in place banking via the internet is the cheapest route for delivering bank products. Second, Sathye (1999) also noted that branch networks as well as staff strength in banks is reduced when internet banking is adopted. This paved a way for the introduction of channels that are self-served as a good number of customers felt that banking at the branch level required a lot of effort and time (Mattila, Karjaluoto, & Pento, 2003). Therefore, the reasons underlying acceptance of online banking are time and cost savings and freedom from place. Customers gain access to their bank accounts, according to Essinger (1999), remotely through the use of a website and then to act out some transactions on their account, while complying with tight checks in security. (Mols, Nikolaj D. Bukh, & Flohr Nielsen) also asserts that by using the Internet a number of banking services such as bill payment and money management services can be offered 24 hours a day. According to Hutchinson and Warren (2003), privacy issues are a source of concern for many Internet users. These issues some of which include collection, being transparent, using and disclosing personal information. Financial institutions have to take advantage of the growth in the users of internet resulting from the mature nature of internet technology recently. There is an increase in demand as many clients and businesses become more sophisticated. The aim of the commercial banks is to increase their share of the market as it redefines its service 18 University of Ghana http://ugspace.ug.edu.gh delivery and maintain competitive in the banking industry. Commercial banks survival and growth in the industry hinges on quality service delivery and product. Internet banking has become a key tool in the service delivery of commercial banks. (Maniraj Singh, 2004) Moody (2002) found that internet banking was a fast growing service that commercial banks provide so that they can retain and gain appreciable share in the market, reduce cost of transaction, and provide better and quicker response to market changes. Maduku (2014), is his study found that the adoption of internet banking was determined mostly by trust in the in the electronic-banking system. He further states banks in South Africa should adopt strategies that are aimed at increasing customers trust in the e-banking system. Banks need to be certain that internet banking platforms are sound technically with the necessary security system to minimise the risk end users may exposed to. Maduku (2014)also states that banks should lobby government to enact laws that will assist in arresting and prosecuting people suspected of internet banking fraud. This will enable clients regain trust in the internet banking. Maduku (2014)also cited apathy as a reason for non-adoption of internet banking in South Africa. Lack of effective communication in other to create awareness and demonstrate the benefits of internet banking and cell phone banking lead to indifference on the part of clients. He further states that banks need to device communication strategies to promote internet banking usage by clients. The study considered customers of four main banks in South Africa in which questionnaires were administered to. The study employed the use of descriptive statistics, Factor Analysis and Multiple Regression Analysis. Also, Massilamany and Nadarajan (2017) noted that trust, self-efficacy and knowledge influenced the internet banking adoption in Malaysia. Their study used simple random 19 University of Ghana http://ugspace.ug.edu.gh technique to administer 200 questions to respondents. They also employed multiple regression analysis and ANOVA to assess the factors that determine adoption of internet banking in Indonesia. Massilamany and Nadarajan (2017) noted a significant relationship existing between the adoption of internet banking and self-efficacy and knowledge. According to Suzanne Harrison, Peter Onyia, and K. Tagg (2014), a client with prior knowledge on the use of the internet and computer and knows the benefits is likely to adopt internet banking. Clemes, Gan, and Du (2012) also had similar findings. They found that customers who have earlier experience of using internet have higher chance of accepting internet banking. Massilamany and Nadarajan (2017)found that trust was part of factors that influenced internet banking adoption. They noted that trust gets the highest attention in electronic commerce as a result of uncertainty and high risk in internet transactions. Trust was found to be a factor that affected client adoption in many services such as online news services by Howard Chen and Corkindale (2008), and health websites by Fisher, Burstein, Lynch, and Lazarenko (2008) and internet banking by Flavián, Guinaliu, and Torres (2005). Trust is divided into two; initial trust and continuance trust. Initial trust is related to the behavior of the client in trust development early stage. Initial trust is affected by a lot of factors. Website is the first category of factors is related to the website. Most clients who do not have earlier experience will depend on the perception according to Prema and Sudhakar (2009) also states that reliability affected internet banking adoption in India. They found that managements of commercial banks should educate clients on the benefits of internet banking products. They also found that security and awareness should be enhanced to attract client’s attention. 20 University of Ghana http://ugspace.ug.edu.gh Prema and Sudhakar (2009) used three models that were thought to have effect on customer acceptance of internet banking in the India in his study. These models are; Usefulness, Ease of use and Reliability. Awareness was seen to have an effect though indirect on internet banking adoption through its influence on the three.They indicated that the rate at which clients adopt internet banking is influenced by the awareness level of the client of internet banking platforms. Sathye (1999) also highlighted that many clients of commercial banks are not aware of internet banking and its unique benefits. Also, Lang and Colgate (2003) found different decisions made by clients regarding alternatives in the market, awareness of the alternative was the reason for the clients to stay with their bank rather than the alternative. The idea was supported by Nui Polatoglu and Ekin (2001). They also found clients of commercial banks fail to adopt internet banking because they do not know of the benefits of the internet banking product. Skill and additional knowledge a client has about internet banking makes it easier for the client to use the product, Nui Polatoglu and Ekin (2001) also noted. Therefore customers of commercial banks who know about internet banking would see internet banking as easier to use, useful, and with great reliability, hence influences how they adopts internet banking. Also, Davis (1993) notes that cost effectiveness and usefulness are factored in when a client decides to adopt new technology proving services as well as goods. Usefulness according to Davis (1993) is the degree to which an individual perceives that using a particular technology will improve his ability to perform. Convenience, effective management of finances and quick services was also noted by Davis (1993) as major factors that affect adopting and using internet banking products comparing to traditional banking services. 21 University of Ghana http://ugspace.ug.edu.gh Ease of use of the internet banking platform is also set to affect the adoption of the product. Ease of use refers to the level to which a person thinks that using a particular system would be effortless. Research done extensively over the past years gives evidence of the important effect of the ease of use, either directly or indirectly through its influence on usefulness (Agarwal & Prasad, 1999). Information technologies that are easy to use are less alarming to the individual (M.-K. Kim, Park, & Jeong, 2004). This means that ease of use is expected to influence positively in customers interaction with internet banking systems. There is also a positive correlation between ease of use and use of consumer technologies, such as computer software(Davis, 1993) in labelling a dimension “ease of use” showed the effect on internet banking adoption. Therefore the easier it is for the customer to use, the more likely is it for the customer to adopt internet banking. Furthermore, there is no trust for internet technology for two specific reasons: Security of the system and concerns about how reliable the internet services are. According to (Lee & Turban, 2001). Security is one common reason that makes individuals unwilling to use internet routes for commerce. This study factors in “Reliability” which is explained as the degree to which internet banking is seen to be safe and reliable” in transmitting financial transactions securely. An individual who may potentially adopt internet banking is not likely to use internet banking if there is a perception of it being unsafe and may create mistakes. (Cook, 2011). Sathye (1999) and Polatoglu and Ekin(2001) assert that for consumers who used electronic banking, the security issue was an important factor. Sathye (1999) also found that security had a positive relation with the use of electronic banking. The present need for banks is not to simply cause a reduction fraudulent activities relating to banking via the internet. Also it is about consumers’ 22 University of Ghana http://ugspace.ug.edu.gh confidence retention and the reliance of customers in their bank and the bank being able to provide channels securely to their money, but also in internet banking as an important pathway for delivery. Therefore reliability would affect positively the adoption of internet banking expectedly. B.-M. Kim, Widdows, and Yilmazer (2005)in their study on the determinants of consumer adopting internet banking found that ability and attitude of consumers and opportunity cost of time play an important role. Their study also showed that literate and younger clients have a greater likelihood of adopting internet banking. They noted that when the individuals’ age related with level, the age effect varied across educational groups. The effect of age on the probability of adopting internet banking is humped-shaped amongst people with lower backgrounds in education. However, those with higher educational background had their probability of adopting internet banking decreasing with age. B.-M. Kim et al. (2005) assumed that consumption behaviour of individuals had basis on their past and present experience (tastes, prices and income), as well as expectations in future. Adding to this basic perspective, the Beckerian theory of consumer behaviour emphasizes time, which cannot be augmented, to explain consumption behaviour. Becker (1971) remodelled the consumption model using the variables commodities and time to produce a specific good. His model explains the association between opportunity cost of time for labour participation and consumption, using the combination of time value and price of commodities within budget restriction. Considering time in the consumption model, effects of time saving products could be investigated within the model. B.-M. Kim et al. (2005)developed their hypothesis regarding demographic factors affects benefits and cost of adopting internet banking along the following; 23 University of Ghana http://ugspace.ug.edu.gh There is the expectation of the existence of connections within technologies such that diffusing any technology is not independent of diffusing another technology (Stoneman and Kwon, 1993). “Internet banking” is one of the technologies that dependents very much on computer networks and an advanced technology over other banking technologies. Bayus (1987) and Norton and Bass (1987) noted that the willingness of a consumer in adopting new technology is affected by a prior pattern of adopting related technologies. The effect of one technology on the next generation of that innovation is expected to be positive especially when the two technologies complement each other. Karjaluoto et al. (2002) indicates that earlier knowledge of computer experience such as Internet, e-mail, and e-payment significantly impacted on online banking usage, and also technology experience, such as ATM, e-ID, teletext, and automats, was also an important factor for attitude toward online banking among Finland bank consumers (Arndt et al., 1985; DeLone, 1988; Igbaria et al., 1995; Karjaluoto et al., 2002; Levin & Gordon, 1989). Lee and Lee (2001) indicated that frequent use of banking service was the most important factor in adopting Internet banking among non-adopters as that can save time and effort conveniently, and prior Internet purchasing behavior was also an important factor, but not as much as the usage of related banking technologies. They employed the use of banking service as a proxy variable indicating a need for banking service. However, it may be hard to adopt recent banking technology when there is no prior experience of banking technologies and even though the thought will be that internet banking is a necessity, lack of comfort and confidence would prevent the use of it. Therefore, in investigating the link between banking technologies, it is best if the effect of the use of related banking technologies like ATM, debit cards and direct payments is studied instead of the use of banking service. 24 University of Ghana http://ugspace.ug.edu.gh Consumers may adopt Internet banking easily if they can use banking technologies and computer software to manage money. There will therefore be an improved efficiency in their use of Internet banking. Even though consumers with have no experience in the use of banking technologies and computer software recognize the benefit of Internet banking, they may be hesitant to adopt Internet banking because it will require more time and money to learn Internet banking. In addition, demographic factors should have an effect on the adoption of Internet banking, Lee and Lee (2001) argued. Age has an effect on the attitude of individuals towards Internet banking and they been able to learn how to invest. Hence they tested the hypothesis that; In addition, higher income earners value time better than low income earners, so consumers with high income can benefit more through the adoption of Internet banking. Also, it is beneficial to consumers with higher levels of financial assets in terms of saving time since they use money transactions more often. Hence the hypothesis was tested. Bartel and Sicherman (1998) stated that individuals who have some form of education need little training to technologically change if they are to learn new technology. Also, Gronau and Hamermesh (2001) investigated variances in demand according to differences in the opportunity costs of various activities. They found that individuals who are well educated have better home productivity than individuals who are less educated because with relatively smaller inputs and time, they can produce household goods. Consequently, the response rate is higher in well-educated than individuals with little education when Internet banking is presented making it advantageous in terms of time and cost saving. Well educated individuals will have the skills to learn quickly hence may have the desire to submit training time to learn how to use “Internet banking”. However, the effect 25 University of Ghana http://ugspace.ug.edu.gh of education on adopting Internet banking should also be dependent on consumer's age. Karjaluoto et al. (2002) revealed that occupation was an important reason for adoption of “Internet banking”. Occupation was split into two categories, white-collar workers and blue- collar workers. White-collar workers were more likely to adopt “Internet banking” than blue- collar workers. Highly paid trained workers had more chance of using advanced technologies (Liu et al., 2001) because their productivity can be enhanced through using advanced technologies within a given time. Their study considered responses from 30 private bank customers. They also interviewed executives of the two top banks in Finland and consultants. The study employed the use of descriptive statistics. The study of Kim, Widdow and Yilmazer (2001) also associates occupation with adoption of “Internet banking” in terms of ability. They contend that consumers with more opportunities to use computer or Internet in their job are more able to use technologies related to computer or Internet than others. Users were put into two groups according to their occupation. Consumers with managerial, professional, and technical jobs are put in the first group. Generally, they possibly make use of the internet or computers regularly in their job, hence they essentially have more skill to use computer or the Internet than those in the other category. Users with service, labor, farming, fishing, and forestry jobs are put in the second category. They more likely to have less opportunity to use computers or the Internet in their job, so their capability of using computers or the Internet might be relatively lesser than the other category. 26 University of Ghana http://ugspace.ug.edu.gh Their study interviewed 4,440 households in other to get responses for the study. Simple random sampling was used to attain the samples. Also, descriptive statistics and probit regression was used in their analysis. Again, to bridge these gaps, this study will employ the use of multi stage sampling technique in other to obtain the samples for the study. Logistic Regression, Factor analysis and a modification to the F test, the F-ratio test for non-parametric analysis of variance. 27 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction This chapter explain the research methodology adopted for the study. The chapter begins with the research design and continues with the philosophical foundation of the research, evaluating the research methods selected, and continues by identifying the reasons for adopting quantitative methods. This chapter includes five main section: Research design, the population of the study, sampling technique and the sample size, data collection, method of data presentation and analysis. 3.2 Research Design To minimise the possibility of a waste of efforts in a study, choosing an appropriate research design for the survey is very important Churchill Jr (1979). The study employed explanatory research design. This was employed to examine the impact of the various factors on the adoption of internet banking products of clients of commercial banks in Ghana. The study examines the factors that impact the adoption of internet banking in Ghana. Clients of commercial banks in the country were considered and questionnaire administered to them. Commercial banks in Ghana were stratified into locally owned commercial banks and foreign owned commercial banks. To this end, the ownership structures of all commercial banks in Ghana were examined. Out of the thirty (30) banks in Ghana as at the end of 2016, sixteen (16) banks were foreign owned whiles fourteen (14) were locally owned (see Appendix D) Due to time and cost constrains, two locally owned banks and three foreign owned banks were selected using purposive sampling to carry out the study. The banks selected were GCB Bank Limited and UniBank Limited representing locally owned banks and Barclays Bank 28 University of Ghana http://ugspace.ug.edu.gh Ghana Limited, Ecobank Ghana Limited and Stanbic Bank Limited representing foreign owned banks. Finally, accidental sampling was used to select clients to administer the questionnaire for the study. Also, the study considered only branches of commercial banks that were situated in Accra because of time constrains. The number of questionnaire administered to clients of each bank was proportionate to the share of deposit of the bank as at the end of the year 2016.To determine the factors that impact the adoption of internet banking in Ghana, Factor Analysis was used. Additionally, logistic regression was also be applied to the data gathered to determine the significant factors that impacts the adoption of internet banking in Ghana. The quality of good research depends on the selection of appropriate research methods (Baker 1994, 109; Silverman 2001, 25). (Lambert & Harrington, 1990) suggested that “the best methods to get high response rates include advance letters or telephone calls, first-class outgoing main and hand-stamped return envelopes, monetary incentives, assurance of confidentiality for sensitive issues, follow-up questionnaires/letters”. Applying an organised questionnaire in this study for the purpose of data collection, this study takes the above recommendations: follow-up calls will be made, and two hot spring spa vouchers are provided in order to increase the likelihood of higher response rates. 3.3 Population The target population for study is clients of commercial banks in Ghana. Both clients of locally owned banks and foreign owned banks in Ghana are considered in this study. The study however limited the coverage to only commercial banks in Accra due to time constraints. Hence only clients of commercial banks in Accra will be considered for this study. 29 University of Ghana http://ugspace.ug.edu.gh 3.4 Sampling Technique and Sample Size Customers Stratification Foreign Banks Local Banks Customers Customers Purposive Sampling ECOBANK BARCLAYS STANBIC UniBank GCB Accidental 122 70 78 63 67 Selected Customers Figure 3.1 Sampling Procedure The researcher adopted multistage sampling technique in other to carry out the research. Firstly, the study used stratified sampling technique to group customers of banks in Ghana into two strata, customers of locally owned and customers of foreign owned banks. The rationale of choosing the stratified sampling technique was based on the premise that clients of locally owned banks experience similar services whiles clients of foreign owned banks also experience similar services. Locally owned banks therefore formed one stratum while foreign owned banks formed another stratum. Secondly, purposive sampling was used to select two banks from locally owned banks and three banks from foreign owned banks. The rationale for selecting three foreign owned banks and two local owned banks was the market share of the various banks in terms of customer 30 University of Ghana http://ugspace.ug.edu.gh deposits for 2016. Three foreign Banks with the highest market share were chosen for the study. These banks were Stanbic Bank, Ecobank and Barclays Bank. Also, the two locally owned banks with the highest market share in deposit were selected. These banks are UniBank and GCB bank. Out of the 30 banks that were operation per the pwc report of 2015, 14 were locally owned and 16 were foreign owned. The study therefore considered 3 foreign owned banks and 2 locally owned banks because of the relative number of each category of banks in the country with the higher number of foreign owned banks given more weight than locally owned banks. Thirdly, proportional allocation was used to assign the number of customers to be used for each bank sampled in the study. The number of clients selected from each bank was proportional to the market share of the bank in question. Banks with higher market share of deposit was given higher questionnaire for its customers. Finally, accidental sampling technique was used to select the clients in each bank to administer the questionnaire. The researcher went to the selected banks at a chosen day and administered the questionnaire to clients who came into the banks to carry out transactions on that day. The researcher assisted clients in filling out the questionnaire. In all, 400 customers from across the five banks were sampled which was made up of 122, 78, 70, 67 and 63 for Ecobank, Stanbic Bank, Barclays Bank respectively. This represents the sampled clients for the foreign owned banks. Whiles GCB Bank and Unibank had 67 and 63 clients sampled respectively. 31 University of Ghana http://ugspace.ug.edu.gh 3.5 Estimation of Sample Size Let N be the population of customers of banks in Ghana which can be divided into L mutually exclusive and exhaustive strata with strata sizes N . Since a random sample is drawn h from each category of banks, the sampling scheme is known as stratified random sampling. Let N be the population size of bank category h h n be the sample size of hth bank category h Since no other information is known about the category of banks except N , the allocation h of a given sample size n was done in proportion to N h Hence, for each bank, the number of clients selected for the study was calculated using N n hh  n N (3.1) The sample size n is calculated using the Yamane (1967) estimation of sample size given by N n  2 1 Ne (3.2) Where e is the margin of error. The number of client base of each bank in Ghana as at the end of 2016 was difficult to ascertain. Hence the study considered the share of deposit of the various banks in the country as a way of determining how many samples should be allocated to each bank sampled. The share of client’s deposit of a particular bank is an indicator of the size of the given bank. 32 University of Ghana http://ugspace.ug.edu.gh The population of Ghana is estimated to be 28.03 million as at the end of 2016 according to estimates of the United Nation. Of this, it is estimated that only about 30 percent have a bank account. Let N be the total number of customers of all commercial banks in Ghana. n be the total sample size of the study N be the total number of clients of foreign banks in Ghana 1 n be the sample size of foreign owned bank customers 1 N be the total number of clients of locally owned banks in Ghana 2 n be the total sample size of clients of locally owned banks 2 Hence the number of individual with bank account 3028.03 N   8.409million 100 Also, per the pwc industry analysis of the banking system in Ghana for 2015, the share of deposits of locally owned banks in Ghana was 32.5% whiles that of foreign owned banks was 67.5% Hence the total number of clients for foreign banks using the percentage market share of deposit is 67.58409,000 N1   5,676,075 100 33 University of Ghana http://ugspace.ug.edu.gh And also 32.58409000 N   2,732,925 2 100 Using the margin of error of 0.05, we calculate the minimum sample size required for this study as follows 8409000 n   399.981 400 184090000.052 Therefore the study sampled 400 clients. To calculate for the minimum sample size for foreign owned banks customers we use the proportional allocation, N n 11  n N 5676075 n1  400  270 8409000 Hence 270 clients of foreign owned banks were selected And the minimum sample size for locally owned banks customers are N n 22  n N 2732925 n2  400 130 8409000 Hence 130 clients of locally owned banks were selected. Also, to select the number of clients from the sampled banks, proportional allocation was use. The sampled clients were selected base on the relative share of the deposit of the bank involved. 34 University of Ghana http://ugspace.ug.edu.gh 11.7 For Ecobank Ghana Limited, the sample size was 270 121.97 122 25.9 6.7 For Stanbic Ghana Limited, the sample size was 270  78.19  79 25.9 7.5 For Barclays Bank limited, The sample size was 270  69.84  70 25.9 8 For GCB bank, the sample size was 130  66.67  67 15.6 7.6 For Unibank Ghana limited, the Sample size 130  63.33  64 15.6 3.6 Data Collection The instrument used for the collection of data for the study was questionnaire. The questionnaire was in two parts or section. The first part was made up of the bio-data of the customers. The second part comprised question on the determinants of “internet banking” adoption. Most of the question were in likert scale form or rated with the remaining few question not rated. All questions were however closed except some few questions. 3.6.1 Source of Data Primary data was obtained from respondents by administering a questionnaire. Clients of commercial banks were made to answer questionnaire regarding bio data and factors that impacts their adoption of internet banking. The questions about the factors were in a likert scale. Commercial Banks in Ghana were grouped into locally owned banks and foreign owned banks. The number of locally and foreign banks to be sampled for the study was ascertained using the numerical strength of each category of bank in Ghana. Hence, two locally owned 35 University of Ghana http://ugspace.ug.edu.gh banks and 3 foreign owned banks were selected for the study. These banks are Unibank and the GCB Bank for locally owned banks while that for the foreign owned banks were Barclays Bank, Stanbic Bank limited and Ecobank Ghana. Due to time and cost constrain, 400 questionnaires was distributed among clients of both locally owned and foreign banks proportional. 3.6.2 Validity and Reliability of Research Instrument Validity and reliability should be considered by a qualitative researcher in order to judge the quality of the study, designing the study and analysing results (Golafshani, 2003). The data was gathered by administering questionnaires to respondents. The questionnaire was developed taking into account existing theories and literature that are relevant to the study. The questions focused on the purpose of the study, research questions and relevant theories in the study area. To ensure that each of the items were correlated a reliability testing was conducted on multiple-items to ensure that the items could be grouped to form a construct(Bell & Bryman, 2007) . The Cronbach‘s alpha test was used. A Cronbachs alpha level of 0.7 and above means there was an acceptable degree. It therefor confirms that the items under each construct have higher correlation or not. 3.7 Method of Data Presentation and Analysis The response categories gathered from clients were in a Likert scales and were ranked in order and therefore referred as ordinal because ordinal scale of measurement is one that conveys order (Jamieson, 2004). In this study, the research used STATA and Statistical Product for Service Solutions (SPSS) for the analysis. 36 University of Ghana http://ugspace.ug.edu.gh All the qualitative data were grouped, quantified and coded to facilitate counting of frequencies of responses that were given by respondents. The data were further edited to ensure that the items were answered correctly to determine their accuracy, consistency, appropriateness of the responses and also to avoid errors and biases. The SPSS software was used to carry out reliability test (Cronbach‘s alpha‘s test for each construct). Finally, the study will also utilise Factor Analysis technique, Logistic Regression modelling and a modification to the F-statistic with 1000 permutation to in order to measure the specific objectives. 3.7.1 Factor Analysis Factor analysis method is used in this study to determine the factors that impacts the adoption of “internet banking” in Ghana. This method can be traced back to the 1900s through the efforts of Charles Spearman and Karl Pearson. It is a model about hypothetical component variable that account for the linear relationship that exist between observed variables. To be appropriately applied, “Factor analysis” requires that the factional relationship between variables is linear and the variables to be analysed have non-zero correlation existing between them. Since the data gathered shows that there is a linear relation between the adoption of “internet banking” and the various factors analysed and the variables being analysed are correlated, hence the use of Factor Analysis in the study. Let 𝑌1, 𝑌2, … , 𝑌𝑛 be the observed adoption of internet banking in Ghana. 𝑋1, 𝑋2, … , 𝑋𝑛 be the common factors that impacts the adoption of internet banking in Ghana 37 University of Ghana http://ugspace.ug.edu.gh 𝜆𝑖1, 𝜆𝑖2, … , 𝜆𝑖𝑛 be the factor-patten loading. This is weights assigned to a common factor indicating how much a unit change in the common factor will produce a change in the 𝑖𝑡ℎ observed variable that impacts the adoption of internet banking in Ghana. 𝜀1, 𝜀2, … 𝜀𝑛 be the unique variable , where 𝑖 = 1,2, … , 𝑛 The factor analytic model can be expressed mathematically as follows Y1  11X1  ... 1r X r 11 Y2  21X1  ... 2r X r  22 Y3  31X1  ... 3r X r  33 .  ........ .... ......... ...... .  ......... .... ......... ...... .  ........ .... ......... ...... Yn  n1X1  ...nr X r  nn We also assume that the common factors and the unique factor variables have zero means and unit variances. That is, E X j   0 , E X 2j  1 and E  j   0 , E  2j  1 . where j 1,2,...,r and i 1,2,...,n . 3.7.2 Components of Variance in Factor Analysis “Factor analysis” is concerned with explaining the total common variance found between variables in terms of the common factors. If there is a non-zero correlation between “Ease of use”, “trust”, “Risk” and the use of internet banking in Ghana, this suggest that there exist some hypothetical components common to these variables. These common factors correspond to the common variables. 38 University of Ghana http://ugspace.ug.edu.gh However, the lack of perfect correlation between the variable also suggest the presence of “unique factors” associated with each variable which correspond to “unique variance”. Additionally, the presence of random error in the measurement of a particular variable also contributes to the unique variance. If this error is uncorrelated with other factors, it will not contribute to the covariance between the variables. Contained in the “unique variance” is the “true variance” which uncorrelated with other observed variables. This is known as “specific variance”. It corresponds to some reliable part of the variable that is found in no other part of the variable. The total variance in a factor analysis model is given by, Total Variance = Common Variance + Specific Variance + Error Variance Where; True Variance = common Variance + Specific Variance Unique Variance = Specific Variance + Error Variance 3.7.2.1 Variance of a variable in terms of its factors From equation 3.1, if 𝑌 is the 𝑗𝑡ℎ variable in terms its factors, the variance 𝜎2𝑖 𝑗 is given by 2 2 j  E Y 2i   E  X  ... X    i1 1 ir r i i  (3.3) This reduces to r r  2j ihikhk  2 2 iik   i (3.4) i k h1 k1 39 University of Ghana http://ugspace.ug.edu.gh Since the correlation between a common factor and a unique factor is always zero, the middle term becomes zero. Thus the variance of a variable in terms of its factors components is given by r r  2    2j ih ik hk  i (3.5) h1 k1 Equation 3.4 is the formula for variance of a variable in terms of correlated factors. If the common factors are uncorrelated, the variance reduces to r  2j  2 ik  2 i (3.6) k1 The communality model for uncorrelated factors is given by r h2 2i ik (3.7) k1 where  2ik is the square of the 𝐾th factor loading of the 𝑖th variable. Also, r 2 2 ik  i (3.8) k1 Correspond to the “unique variance” of the 𝑖th variable. Since we defined the variable 𝑌𝑖 to be expressed in the standard score form, it follows that 𝜎2𝑖 = 1 Consequently the following equation can be developed h2 1 2i 1 (3.9) 40 University of Ghana http://ugspace.ug.edu.gh  2 1 h2 (3.10) i i The Unique variance contains true specific variance and random error variance unique to specific variable. This is expressed as follows  2  s2  e2 (3.11) i i i where 𝑠2𝑖 is the specific(true) variance of the variable 𝑖 𝑒2𝑖 is the random error variance unique to variable 𝑖 It follows that the reliability of the variable 𝜌𝑖𝑖, is the sum of the two components of the of the true variance ii  h 2 i  s 2 i (3.12) Hence the communality of a variable is always less than or equal to the reliability of the variable. 3.7.2.2 Correlation between Two Variables in Terms of the Factors For any two variable 𝑌𝑖 and 𝑌𝑚expressed in a normalised form, the correlation between the, 𝜌𝑖𝑚, is equivalent to im  E YiYm  (3.13) After substitution and expansion, the correlation between variable in terms of correlated factors becomes r r im ikmhhk (3.14) h1 k1 41 University of Ghana http://ugspace.ug.edu.gh This contains no term corresponding to the unique factors because none of the nonzero weights for the unique factors matches with the nonzero weights of another unique factor during derivation. If the common factors are uncorrelated, the equation reduces to r r im ikmh (3.15) h1 k1 3.7.3 Factor Extraction The Principal-Axes method of factor extraction was used in extracting the factors that impacts the adoption of “internet banking” in Ghana. This method seeks to find the most representative score derived from the observed scores. 3.6.3.1 General Computing Algorithm for Finding Factors Let 𝑌 be an 𝑛 × 1 vector of the adoption “internet banking” in Ghana and the observed random variable of rate of internet banking adopted be 𝑌1, 𝑌2, … , 𝑌𝑛. Assuming 𝐸(𝑌) = 0, 𝐸(𝑌𝑌΄) = 𝑅𝑌𝑌 is the correlation matrix with unities for the variances in its principal diagonal. Let 𝑋 be an 𝑟 × 1 random vector of factors impacting the adoption of internet banking in Ghana whose variables are observed common factors 𝑋1, 𝑋2, … , 𝑋𝑟 Assuming 𝐸(𝑌) = 0 and 𝐸(𝑋𝑋΄) = 𝑅𝑥𝑥 is the correlation matrix Let also 𝑬 be an 𝑛 × 1 random vectors whose variables are unique factors 𝜀1, 𝜀2, … , 𝜀𝑛, having the property 𝐸(𝑬) = 𝟎 and 𝐸(𝑬𝑬΄) = 𝑰. 42 University of Ghana http://ugspace.ug.edu.gh Also, let 𝚲 be an 𝑛 × 𝑟 matrix of common factor-pattern coefficient and 𝜓 and 𝑛 × 𝑛 diagonal matrix of unique factor-pattern coefficients whose diagonal elements are 𝜓1, 𝜓2, … , 𝜓𝑛 Then Y X E (3.16) The equation above is the fundamental equation of “factor analysis”. This indicates that the adoption of internet banking in Ghana is weighted combination of the common factors in 𝑋 and the unique factors in 𝐸. Substituting the equation above into E YY '   RYY we have ' RYY  E X E X E     E X E  ' X '  'E '    E XX ' ' XE ' ' EX ' ' EE '  '   R  'xx RXEREX ' 2 But because the common factors are assumed not to be correlated, R XE  REX  0 Hence R  R 2YY xx (3.17) Equation () is the fundamental theorem of “factor analysis” in matrix notation. Because 𝛹2 is a diagonal matrix, subtracting it from 𝑅𝑌𝑌 only affects the principal diagonal of 𝑅𝑌𝑌, leaving the off-diagonal elements unaffected. Hence the off-diagonal correlations are due to only the common factors. The principal diagonal of 𝑅𝑌𝑌 − 𝛹 2 contains the 43 University of Ghana http://ugspace.ug.edu.gh communalities of the variables. These are the variances of the observed variables due to just the common factors. 𝑅𝑌𝑌 − 𝛹 2 is called the reduced correlation matrix. On the other hand if we subtract Λ𝑅𝑥𝑥Λ ΄ from 𝑅𝑌𝑌 R R  '  2 YY xx (3.18) Equation 3.6 represents the partial covariance matrix among the adoption of “internet banking” in Ghana, when the common factor holds, it is a diagonal matrix. Once all the common-factor parts of the observed variables have been partialled from them, there remains no correlation between the respective residual variable. The predicted adoption of internet banking in Ghana from the common factors in 𝑋 Yˆ  R R1x YX XX X (3.19) We can derive the variance-covariance matrix as follows E YˆX   E R 1 ' 1YX RXX XX RXX RXY   R R1 R R1YX XX XX XX RXY  R  ' XX (3.20) Λ = 𝑅𝑌𝑋𝑅 −1 𝑋𝑋 represents the transpose of the regression weight matrix for predicting the observed variable from the common factors. Each of the coefficients in a row of Λ represents how much a unit change in common factors produces a change in the variable corresponding to that row. There are two kinds of matrices that reveal relationships between the adoption of internet banking and factors. The first is Λ = 𝑅 𝑅−1𝑌𝑋 𝑋𝑋 which is referred to as the factor-pattern matrix. The coefficients of a factor-pattern matrix are weights to be assigned to the common factors 44 University of Ghana http://ugspace.ug.edu.gh in deriving the adoption of internet banking as a linear combination of the common and unique factors. The factor-pattern coefficients are equivalent to regression weights for predicting the observed variable from the common factors. The second matrix is known as the factor-structure. The coefficients of this matrix are the covariances between the adoption of internet banking and factors. It is derived as follows; 𝑅 ΄𝑌𝑋 = 𝐸(𝑌𝑋 ) = 𝐸[(Λ𝑋 + Ψ𝐸)𝑋΄] = 𝐸(Λ𝑋𝑋΄) + 𝐸(Ψ𝐸𝑋΄) = Λ𝑅𝑋𝑋 + Ψ𝑅𝐸𝑋 = Λ𝑅𝑋𝑋 (3.21) Since 𝑅𝐸𝑋 = 0. Assuming we know the score and the unique-factors. This will make it possible to use multiple regressions to illustrate the method of extracting one factor at a time. Assuming also that we have an 𝑛 × 1 random vector 𝑬 of unique factor variables. Let us partial the unique factors from the 𝑛 × 1 vector 𝑌 on 𝑛 observed variables. The matrix of the covariance matrix is among the unique factors is an identity matrix 𝐼 because it is assumed that the unique factors have zero means and standard deviation of 1 and are mutually uncorrelated. The matrix of covariance between the adoption of internet banking adoption, Y, and the unique factors in 𝐸 is given by 𝑅𝑌𝐸 = 𝐸(𝑌𝐸 ΄) = 𝐸[(Λ𝑋 + Ψ𝐸)𝐸΄] 45 University of Ghana http://ugspace.ug.edu.gh = Λ𝐸(𝑋𝐸΄) + Ψ𝐸(𝐸𝐸΄) = Λ𝑅𝑋𝐸 + Ψ𝐼 = Ψ (3.22) Because 𝑅𝑋𝐸 = 0 For any observed adoption of internet banking, 𝑌𝑐 Let 𝑌𝑐 = 𝑌 − Ψ𝐸 From this we obtain 𝑣𝑎𝑟(𝑌𝑐) = 𝑅𝑐 = 𝐸(𝑌𝑐𝑌 ΄ 𝑐) = 𝐸[(𝑌 − Ψ𝐸)(𝑌 − Ψ𝐸)΄] = 𝑅𝑌𝑌 − 𝑅𝑌𝐸Ψ − Ψ𝑅𝐸𝑌 + Ψ. Ψ = 𝑅 − Ψ2𝑌𝑌 (3.23) Hence 𝑌𝑐 contains variables dependent on only the common factors. We can use the 𝑌𝑐 to derive the first factor however, there is no unique factor solution for this. Depending on the method used, different solutions can be obtained. However each method will yield an 𝑛 × 1 vector 𝑏1 such that the first common factor 𝑓1 will be found as a linear composite of the variable 𝑌𝑐. This is because 𝑌𝑐is assumed to be a linear combination of 𝑟 common factors and the variables in 𝑌𝑐 and 𝑟 are in the same space. Hence our first factor will be 46 University of Ghana http://ugspace.ug.edu.gh 𝑓1 = 𝑏 ΄ 1𝑌𝑐 (3.24) 2 For 𝑓1 to be normalised to have a unit variance, 𝐸(𝑓1 ) = 1. Suppose that 𝛽1 is an 𝑛 × 1 weight matrix which combines the variables in 𝑌𝑐 to produce a composite corresponding to the factors. The variance –covariance matrix of Y is given by c Var Yc   Rc  R 2YY    21   ' 1 RYY 2 1 Hence b1    1  1   (3.23)  1  Having found f , we now find the first n1 column  of the factor pattern matrix  .if we 1 1 assume we are working with uncorrelated factors, then the correlation of the variables with f will give us  . Hence 1 1 1  E Yf1  (3.24) But f '1  b1Yc Hence  '1  E YYcb1  ' 1  E Y Y E b1  47 University of Ghana http://ugspace.ug.edu.gh '   E YY ' YE ' b   R 2 b (3.26) 1   1 YY 1 The first column,  , of the factor pattern matrix, , is equal to the approximate linear 1 transformation applied to the correlation matrix with the unique variances partialled from it. We then proceed to find the second factor f . In other that f be uncorrelated with f , we 2 2 1 partial out f from the vector Y . The covariance matrix between Y and f is given by 1 c c 1 E Yc f1   E Y E  f 1  E Yf1 Ef1  But because the correlation between the common factors and the unique factors is zero we have E Yc f1   E Yf1   1 (3.28) We define the first residual variable vector Y as 1 Y 1 Yc 1 f1 Hence the matrix of covariance among the first residuals will be given by E Y ' 1Y1   E Yc 1 f 1 Y ' c  f  '  1 1    E Y Y '  E Y f ' ' 'c c c 11  E 1 f1Yc  E 1 f1 f1   R 2 ' ' 'YY  11 11 11   R 2   'YY 1 1  48 University of Ghana http://ugspace.ug.edu.gh As before, finding the second factor will involve finding some weight matrix that transforms the residual variables into the next factor. If b is the second n1 weight vector, then 2 f  b'Y 2 2 1 1 The column vector b can be seen as some other vector multiplied by a scaler 2 2  2  1  b2  2    2  Where  22  2 RYY 2 1 '1 2 But f is not correlated with1 f . That is 2  f f  E  f f ' 1 2 1 2   E  f1Y1b2   E  f  1 Y ' ' c  f11 b 2   E  f1Y 'c E  f1 f1  ' 1 b2   Substituting E Yc f1   E Yf1   1 , we have,  ' 'f f  1 1 b2  0 1 2 Hence the two factors are not correlated. In finding the second factor, we find the second column of the factor pattern matrix 2  which for uncorrelated factors gives the correlation between Y and f : 2 49 University of Ghana http://ugspace.ug.edu.gh 2  E Yf2  '  E Y Y E  f  b   1 1 2   RYY 2 1 '1 b2 Generally in factor analysis, we extract one factor at a time. We find that the pth column of the factor loading matrix p for the pth common factor f p extracted from the scores in Y c is given by   R 2p YY 1 '1  ...p1 'p1 bp Where bp is the weight vector required to find the pth factor and it is given by  1  bp   p     p  (3.29) Where  2   ' R 2p p YY 11  ...p1p1  p 3.7.3.2 The Principal-Axes Method of Factoring Let Y be an n1 standardized random vector whose coordinates are n variable. We wish to find a linear composite of X by weighing the variables in Y with a weight of vector  , that 1 is X   'Y 1 (3.30) 50 University of Ghana http://ugspace.ug.edu.gh Also,  2 2X  E(X1 )   ' RYY 1 Is the maximum given the constrain,  ' 1 . This constrain enable us to find a linear variable having the greatest possible variance under the restriction that the sum of the squares of the weights used to find the composite equals 1. Without it the weights in β could be allowed to get larger and larger without bound, allowing the variance to increase without bound. Hence there would be no definite maximum variance. Let us find the weight vector of β that serves as a weight vector in equation 3.28. This involves finding the maximum of a function, we therefore resort to derivative calculus. Also, because of the constrain placed on the solution, we have to use the Lagrangian multipliers. The function F of the weights in β to maximise is F  E X 2    '1 E YY '    ' 1   'R    ' 1 YY (3.31) Where  is a Lagrangian multiplier and R is an n×n correlation matrix of the variable Y. YY Note that  'R   tr  'R  '  and  'YY YY    'I . Taking the partial derivative of F with respect to the weights vector is β, we get, F  2RYY  2  F At maximum,  0 hence ,  51 University of Ghana http://ugspace.ug.edu.gh RYY  I   0 The equation above represents the system of equation in n unknowns. But we cannot solve the equation by finding the inverse of RYY  I  . The solution will be trivial   0 and it will violate the constraint that  ' 1 Hence we must not be able to find the inverse of the RYY  I  . This will imply that RYY  I  is a singular matrix. To be singular, it must have a determinant of RYY  I  0 That is 11  1n  0 n1 nn  Expanding the determinant of RYY  I will results in the polynomial equation in  . The term of the highest order in  will come from the product of the diagonal element of the n determinant. Hence   is the highest order term of the polynomial and RYY  I is a polynomial of degree n. Let f   be the polynomial, then n n1 f      bn1    ...b1  b0  0 (3.32) 52 University of Ghana http://ugspace.ug.edu.gh The equation above is the characteristic equation of the matrix R and f   is known as YY the characteristic polynomial for R with n roots. The roots of  , ,..., are known as the YY 1 2 n characteristic roots or the eigenvalues of R . YY Also, multiplying the equation RYY  I   0 by ' we get  'R ' 'YY    0  'RYY   ' If we take any  satisfying R 'YY  I   0 and the constrain   1 then the above equation becomes  'R   (3.33) YY This is an equation for the variance of the composite random variable. 3.8 The Logistic Regression Model In other to determine the chance of a customer adopting internet banking given a particular factor, the logistic regression model will be employed. The Logistic regression model is generally used to model the outcomes of a categorical dependent variable. The response variable is not measured on a ratio scale and the error terms are not normally distributed. Since the adoption of internet banking data is categorical in nature and the error terms are not normally distributed, the logistic regression can be used determine the factors that impact the adoption of internet banking. 53 University of Ghana http://ugspace.ug.edu.gh For purposes of interpretation and in line with the binary logistic techniques, customers who use internet banking facility were considered the reference variable and according assigned a value of 1 and non-users were assigned zero. Let Z be a random variable that can take on one of two possible value, either a client uses internet banking or not, for a data set with a total sample size of say M . Each observation is independent. Z is considered as M binomial random variables Z . The value of 1 is i considered a “success” of adoption of internet banking while the value of 0 is considered as “failure”, clients who do not have internet banking. 𝑁 be the total number of population and 𝑛 be the column vector with elements 𝑛𝑖 representing the number of observations in population 𝑖 where the total sample size is 𝑁 ∑ 𝑛𝑖 𝑖=1 𝑌 be a column vector of length 𝑁 where each element 𝑌𝑖 is a random variable representing the number of success of 𝑍 for population in i . Also, let y represents the observed counts of the number of successes for each population i and  be a column vector of let N with elements   P(Z 1/ i) which is the probability i i of success for any given observation in the ith population. The linear components of the model contain the design matrix and the vector of parameters to be estimated. The design matrix of the independent variables, X , is composed of N rows and K 1 columns, where K is the number of independent variables specified in the model. For each row of the design matrix, the first element X i 1. This is the intercept say  . The 0 54 University of Ghana http://ugspace.ug.edu.gh parameter vector,  , is a column vector of length K 1 . There is one parameter corresponding to each of the K columns of independent variables settings in X plus one  0 as intercept denoted by  . The logistic regression model equates the logit transform, the log-odds of the probability of success, to the linear components.    K log i   xikk 1  k0 (3.34) i i 1,2,..., N 3.8.1 Parameter estimation The goal of the model is to estimate the K 1 unknown parameters  . This is done by using the maximum likelihood estimation which entails finding the parameters for which the probability of the observed data is greatest. The maximum likelihood equation is derived from the probability distribution of the dependent variable. Since each y represents a binomial i count in the ith population, the joint probability density function of Y is N n !    i  y n  y f y  ii 1 i  i i i1 y !n  y ! i i i (3.35)  ni  For each population, there are   different ways to arrange y successes from among n i i  yi  y trials. The probability of y success is  ii since the probability of a success for any one of i n  y the n trials is  . Also, the probability of n  y failures is 1i i i i i  i i Thus, the likelihood function is given by 55 University of Ghana http://ugspace.ug.edu.gh N n ! y n L  / y  i  yi  i  ii 1  i  i1 yi ! ni  yi ! The factorial terms are constants since they do not contain  hence they can be ignored i y N i   n L  / y  i 1  i  i 1 i1  i  (3.36) From equation 3.32 K   x ikk  ek0   i   K    xikk 1 ek0   Substituting  into equation 3.8 we have, i K ni K yi   xik k N   x ikk  ek0  L  / y ek0  1 K  i1       xik  1 ek0   K K ni N  yix   ik k xikk e k0 1 ek0  i1       Taking the log, we get K N K     xikk l    yi  xikk   n .log 1 ek0 i i1  k0      (3.37) Differentiating with respective  , we get 56 University of Ghana http://ugspace.ug.edu.gh K l   N  1  xikk  yi xik  ni . . 1 ek0  K k i1 x    ik k k 1 ek0   N  y . i xik  ni ik xik i1 l   The maximum likelihood for  can be found by setting  0 and finding the value of k each  . Note that  is a function of k ik k Thus, N log it  i   i xi . (3.39) i1  The quantity i  odds for the individual x i 1 i 3.9 Non-Parametric Multivariate Analysis of Variance In comparing the internet Banking Adoption of clients of commercial banks in Ghana, the non-parametric multivariate analysis of variance was employed. This was used because the multivariate test of normality was carried out using the Royston normality test which resulted in the finding that the factors extracted were not multivariate normally distributed. The Royston normality test assesses the hypothesis that: H : The factors are multivariate normally distributed 0 H : The Factors are not multivariate normally distributed 1 The null hypothesis was rejected, hence the factor extracted were not multivariate normally distributed. 57 University of Ghana http://ugspace.ug.edu.gh Because the multivariate normality assumption of MANOVA was not met, the study used the one way non-parametric multivariate analysis of variance. The essence of the test was to compare the location parameter (median) between the categories of banks. Firstly, the test statistic is constructed. The hypothesis for the test is given by H : There is no difference in the location parameter between the categories of banks 0 H : There is difference in the location parameter between the categories of banks 1 Let N be the total number of observations dij be the distance between observation i 1,2,3,..., N and j 1,2,3,..., N The total sum of square is given by 1 N1 N SST   d 2 ij N i1 ji1 (3.39) Also the within bank sum of squares or residuals is given by 1 N N SSW   d 2 ijeij n i1 ji1 (3.40) Where e takes the value of 1 if i jij and are in the same group or zero otherwise. Then SS  SS SS , Where SS is the group sum of squares. A T W A The pseudo F-ratio to test the multivariate hypothesis is given by 58 University of Ghana http://ugspace.ug.edu.gh SSA a 1 F  SSW N  a (3.41) Secondly, the p-value is ascertained, If the null hypothesis is true, and the adoption of internet banking across the category of banks are the same, then the observation under each category of bank can be exchanged among the other category. Thus, the random permutation of observation of locally and foreign owned banks is carried out. This leads to the calculation of a new value of F say F * comparing the values computed, the p-value is then computed as No.of .F*  F  p  Total.No.of .F*  (3.42) Note that the original F computed is a member of the distribution of F * . The P-value obtained is a measure of confidence in the null hypothesis (Freedman and Lane 1983). We reject the null hypothesis if the P-value is less 0.005. 59 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR ANALYSIS AND RESULTS 4.1 Introduction Internet banking is product of commercial banks that is made available to its clients to enable them conducts transactions without necessarily going to the banking halls. It enables clients also conducts banking transactions at their own convenience. Banks are also able to reduce cost in terms of stationary and even staff strength when clients adopt internet banking as opposed to the traditional branch banking. This study sought to examine the key factors that impact the adoption of internet banking in Ghana. The following specific objectives were set for the study. 1. To examine the factors that significantly contributes to customer’s adoption of internet banking. 2. To determine the chance of a customer’s adoption of internet banking given some significant factors. 3. To compare internet banking adoption between international banks and local banks in Ghana. To examine the objectives above, commercial banks in Ghana were stratified into locally owned and foreign owned banks using their ownership structure which indicates persons and entities that own the various commercial banks in Ghana. Majority shareholders of banks that were local was classified as locally owned banks and that of foreign was classified as foreign owned banks. Based on the total number of each category of banks in Ghana, a sample of 3 foreign owned banks and 2 locally owned banks were selected for the study. 60 University of Ghana http://ugspace.ug.edu.gh A questionnaire was design to gather responses from clients of the various banks in Ghana. 400 clients were sampled for the study with a total of 130 sampled from locally owned banks and 270 from foreign owned banks based on proportional allocation. 4.1.1 Characteristics of sample Even though the selections of the samples were random, more males were samples than females for the study. Out of the 400 sampled clients of commercial banks in Ghana, 61.5% were males whiles 38.5% were females as shown in the table below. Table 4.1: Gender Distribution Gender Frequency(n) Percentage (%) Male 246 61.5 Female 154 38.5 Total 400 100.0 Also, clients in the age category of between 20 and 30 years were sampled more than any other age category with a total percentage sampled of 49.75% while clients in the age category of 50 years and above were the least sampled for the study. A total of 4% of the sampled clients were in the age category of 50 years and above. A sizeable number of clients in the age category of 30 and 40 years were also sampled for the study. A total of 34.5% of the entire sampled clients were in the age category of between 30 and 40 years. Below is the table of the age distribution. Table 4.2: Age Distribution Age Frequency Percent Less than 20 years 25 6.25 20-30 Years 199 49.75 31-40 years 138 34.50 41-50 years 22 5.50 51 years and above 16 4.0 Total 400 100.0 61 University of Ghana http://ugspace.ug.edu.gh The sampled clients were also comparatively more educated. 38% of the sample clients had a minimum educational level of a Bachelor degree whilst clients with the minimum educational level of Junior High School and Doctorate degrees were the lowest sampled with percentages of 1 and 1.3 respectively. Below is the distribution of minimum educational level of the sampled clients. Table 4.3: Educational Level Highest Educational Level Frequency Percent JHS 4 1.0 SHS 48 12.0 Diploma 88 22.0 Bachelor 155 38.8 Masters 76 19.0 PhD 5 1.3 Professional 24 6.0 Total 400 100.0 Also, clients working in private organizations/entrepreneurs and students were sampled more for the study. A total of 32% and 36% of the entire samples were samples from these categories respectively. Table 4.4: Occupation Current Occupation Frequency Percent Student 144 36.0 Public servant 102 25.5 Civil servant 24 6.0 Private/Entrepreneur 128 32.0 Housewife 2 0.5 Total 400 100.0 The clients sampled also consist of 64.3% of clients using at least one of the internet banking products of commercial banks whilst 35.7 of clients do no use any of the internet banking products. 62 University of Ghana http://ugspace.ug.edu.gh Table 4.5: Usage of Internet Banking Do you use Internet banking? Frequency Percent Yes 257 64.3 No 143 35.7 Total 400 100.0 A cross tabulation of sex and usage of internet banking reveals that 66.7% of males sampled for the study used internet banking whiles 33.% where non users of the service. Also, out of the total number of females sampled for the study, 60.4% of them were users of the internet banking platform whiles 39.6 were non users of the service. Table 4.6 Cross tabulation of Sex and Usage of Internet Banking Do you use internet banking Sex Yes No Total Male Count 164 82 246 % within Sex 66.7% 33.3% 100.0% % within Usage 63.8% 57.3% 61.5% Female Count 93 61 154 % within Sex 60.4% 39.6% 100.0% % Within Usage 36.2% 42.7% 38.5% Total Count 257 143 400 Also, a cross tabulation of age and usage of internet banking shows that the highest within age group users of internet banking was the age group of 50 years and above recording 100% usage within the age group. This was followed by the age group between 31 and 40 years recording 80.4% users as against 19.6% non-users in the same age group. The lowest within age group users where the age group less than 20 years recording 20% users against 80% non- users. 63 University of Ghana http://ugspace.ug.edu.gh Table 4.7 cross Tabulation of Age and usage of internet banking Do you use internet banking Age Total Yes No Less than 20 years Count 5 20 25 % within age 20.0% 80.0% 100.0% % within Usage 1.9% 14.0% 6.3% 20-30 Years Count 108 91 199 % within age 54.3% 45.7% 100.0% % within Usage 42.0% 63.6% 49.8% 31-40 years Count 111 27 138 % within age 80.4% 19.6% 100.0% % within Usage 43.2% 18.9% 34.5% 41-50 years Count 17 5 22 % within age 77.3% 22.7% 100.0% % within Usage 6.6% 3.5% 5.5% 51 years and above Count 16 0 16 % within age 100.0% 0.0% 100.0% % within Usage 6.2% 0.0% 4.0% Total Count 257 143 400 A cross tabulation of Educational level and usage of internet banking products reveal that sampled individuals whose highest qualification was masters level had more users of internet banking within that category recording 96.1% whiles non-users in that educational level was only 3.9%. Individuals whose highest education level was Senior High School had the lowest users of internet banking users within that educational level. This recorded 29.2 users against 70.8 non-user. 64 University of Ghana http://ugspace.ug.edu.gh Table 4.8 Cross Tabulation of Educational Level and Usage of Internet Banking Usage of internet banking Total Educational Level Yes No JHS Count 2 2 4 % within Educational 50.0% 50.0% 100.0% Level % within Usage .8% 1.4% 1.0% SHS Count 14 34 48 % within educational 29.2% 70.8% 100.0% Level % within Usage 5.4% 23.8% 12.0% Diploma Count 44 44 88 % within Educational 50.0% 50.0% 100.0% Level % within Usage 17.1% 30.8% 22.0% Bachelor Count 102 53 155 % within Educational 65.8% 34.2% 100.0% Level % within Usage 39.7% 37.1% 38.8% Masters Count 73 3 76 % within Educational 96.1% 3.9% 100.0% Level % within Usage 28.4% 2.1% 19.0% PhD Count 3 2 5 % within Educational 60.0% 40.0% 100.0% Level % within Usage 1.2% 1.4% 1.3% Professional Count 19 5 24 % within Educational 79.2% 20.8% 100.0% Level % within Usage 7.4% 3.5% 6.0% Total Count 257 143 400 4.2 Factors Influencing the Adoption of Internet Banking To determine the factors that contributed to the adoption of “internet banking in Ghana”, the exploratory factor analysis was used to the extract factors. The principal axis method of 65 University of Ghana http://ugspace.ug.edu.gh factoring was used. Also because of the correlation between the variables as shown in the correlation matrix in appendix, the direct oblimin rotaion was adopted since it allows for correlation between variables. 4.2.1 Correlation Matrix The correlation matrix in appendix C indicates how each of the 58 items associates with each other. From the correlation matrix, it can be seen that some of the correlation are relatively higher whiles some are relative low even zero. Relatively high correlation suggests that there is a relationship between variables whiles relatively low correlation on no correlation is an indication that there is no relationship between the variables. Also, relatively high correlation between variables is an indication that factor analysis will probably group the variables in question into a factor. They normally would have higher loadings on the same factor. Variables with lower correlation are however an indication that they will have lower loadings on the same factor. Table 4.9: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.87 Bartlett's Test of Sphericity Approx. Chi-Square 17728.418 Df 1653 Sig. 0.000 To determine whether or not enough items are predicted by each factor in the model, a Kaiser-Meyer-OIkin (KMO) test was carried out. A KMO greater that 0.7 is an indication that enough items are predicted by each factor in the model. Since the KMO test for the model was greater than 0.7 (0.870>0.7), each of the factors extracted by the use of the principal axis method of extraction was able to predict enough items in the model. 66 University of Ghana http://ugspace.ug.edu.gh Additionally, the Bartlett’s test of sphericity was carried out to ascertain whether the variable were correlated enough to warrant factor analysis. It used to test that the correlation matrix has significant correlation among the variables. The observed correlation matrix is expected to have small off-diagonal coefficients if the variables are independent. Hypothesis: H : The correlation matrix is an identity matrix o H : The correlation matrix is not an identity matrix 1 The Bartlett’s test of sphericity tests the hypothesis that the correlation matrix is an identity matrix. The hypothesis is rejected if the significance value is small (P<0.05). Since the P- value from the Bartlett’s test of sphericity above is small yielded 17728.418 with a p-value of 0.00<0.005, we reject the hypothesis that the correlation matrix is an identity matrix. Hence the correlation matrix is significantly different from an identity matrix. Therefore the variables are correlated enough to warrant factor analysis. 4.2.2 Number of Factors to Retain To determine the number factors to retain, the scree plot was used. It is a plot of the eigenevalues against the factors. The screen plot shows a plot of the eigenevalues verses the components of the factors using the principal axis method of factoring. The scree plot below shows that 12 factors were significant in determining the internet banking adoption in Ghana 67 University of Ghana http://ugspace.ug.edu.gh Figure 4.1: Scree Plot However, the parallel analysis indicates that only nine factors were significant in determining the adoption of “internet banking” in Ghana. Based on the parallel analysis conducted, nine factors were therefore extracted using the principal axis method of factoring with the Direct Oblimin rotation method. The Direct Oblimin method was used because it allows for correlation among the variables as in this case. The sum of squares (SS) loadings for the various factors is shown in the table below. 68 University of Ghana http://ugspace.ug.edu.gh 4.2.3 Grouping of Components into Factors The components were grouped into factors using the factor pattern matrix. The items in factor matrix cluster into groups which had higher loadings. Nine groups of components or factors were extracted using the factor pattern matrix and the factor analysis image below. The grouped components were renamed as Trustworthiness of the Bank, Usefulness of internet banking, Risk, Accessibility, Ease of use, Assurance in the banks website, Service Visibility, Awareness of benefits of internet banking and Trust in internet banking as a solution to the banking needs. The Factor, ‘Trustworthiness’ in the bank was found to be the accelerating factor as it accounted for the highest correlation among the variables in the first factor. This was followed by ‘Usefulness’, ‘Risk’, ‘accessibility’, ‘Ease of use’, Assurance in the banks website, Service Visibility, Awareness of benefits of internet respectively. 4.2.4 Reliability Analysis of the Grouped Factors Table 4.11 Reliability Statistics N of Factor Cronbach's Alpha Items 1 .921 15 2 0.826 9 3 0.683 9 4 0.914 7 5 0.826 3 6 0.841 4 7 0.896 4 8 0.916 4 9 0.804 3 In other to test whether grouped factors were correlated enough to be grouped together to form a factor, a reliability analysis was conducted. Since all the Cronbachs Alpha for the 69 University of Ghana http://ugspace.ug.edu.gh various factors were more than 0.7, we conclude that the factors were correlated enough to be grouped as factors. Table 4.12: Table Total Variance Explained Rotation Extraction Sums of Squared Sums of Factor Initial Eigenvalues Loadings Squared Loadingsb % of Cumulative % of Cumulative Total Total Total Variance % Variance % 1 25.989 44.809 44.809 25.706 44.320 44.320 15.295 2 5.127 8.839 53.648 4.826 8.320 52.641 13.083 3 2.560 4.413 58.062 2.188 3.773 56.414 5.348 4 2.325 4.008 62.070 2.061 3.553 59.967 13.793 5 1.778 3.065 65.135 1.491 2.570 62.537 6.554 6 1.605 2.767 67.903 1.315 2.266 64.803 11.602 7 1.365 2.353 70.256 1.081 1.864 66.667 14.390 8 1.273 2.196 72.451 .982 1.693 68.360 4.385 9 1.139 1.964 74.415 .810 1.396 69.756 4.778 10 1.024 1.766 76.181 11 .964 1.663 77.843 Extraction Method: Principal Axis Factoring. a. Only cases for which Do you use internet banking = Yes are used in the analysis phase. b. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance. Table 4.9 above consist of the components, the initial eigenevalues associated with each factor, and the extraction sum of squares loadings for the nine factors extracted. Of the 58 components analyzed, nine factors were found to be significant in the adoption of internet banking of clients of commercial banks in Ghana. The selection criterion was based on the Scree plot and the parallel analysis of the eigenevalues (Monte Carlos Simulation of the eigenevalues). The Scree plot retained 12 factors while the parallel analysis found out of the 12 factors only 9 were significant in the adoption of internet banking in Ghana. 70 University of Ghana http://ugspace.ug.edu.gh Table 4.9 indicates, that the nine factors listed above cumulatively accounts for 74.415% of the variability in internet banking adoption in Ghana using the sampled data set before rotation. Trustworthiness, which is the accelerating factor, accounts for 44.809% of the variability while Usefulness, Risk, Accessibility Ease of use, Assurance in the banks website, Service Visibility, Awareness of benefits of internet banking and Trust in internet banking as a solution to the banking needs accounted for 8.839, 4.413, 4.008, 3.06, 2.767, 2.353, 2.196 and 1.964% of the variability effect on internet banking adoption in Ghana respectively. The other 49 components together accounted for a total of 25.585% of the variability in the data set before rotation. After rotation, the percentage of variance accounted by the nine factors cumulatively was 56.756%. This was more than the rest of the 49 components. Trustworthiness accounted for 44.32% whilst usefulness, Risk and Accessibility, Ease of use, Assurance in the banks website, Service Visibility, Awareness of benefits of internet banking and Trust in internet banking as a solution to the banking needs accounted for 8.32%, 3.773%, 3.553 and 2.570, 2.266, 1.864, 1.693, and 1.396 respectively. Additionally, Chi-square test of association was used to ascertain whether there was a relationship between the adoption of internet banking in Ghana and the various demographics from the sampled clients. Table 4.13 below shows results of the Chi-square test of association between the demographics and the adoption of internet banking in Ghana. Table 4.13: Test of Association Degree Chi P- Demographics of Square value Freedom Gender 1.3629 1 0.243 Age 56.203 4 0.000 Education Q 69.847 6 0.000 Occupation 80.025 4 0.000 71 University of Ghana http://ugspace.ug.edu.gh There was a relationship between Age, Education qualification and occupation of a client and adoption of internet banking. The p values of these demographic factors are less than α=0.05 hence there exist a relationship between the factors and internet banking. However, the p value for the Chi-Square test of association between Internet banking adoption and gender was greater than α=0.005. Therefore, no relationship exists between internet banking adoption of clients and gender from the sampled clients of commercial banks in Ghana. 4.3 Chance of a Customer Adopting Internet Banking Given a Particular Factor To determine the chance of a customer adopting internet banking given a particular factor, the binary logistic regression was used to analyse the factors extracted. The binary logistic regression was used because the response variable was categorical and dichotomous. The response variable takes the value of 1 with probability of success p, or 0 with the probability of failure 1-p. To ascertain whether the logistic regression model was fit, the Hosmer-Lemeshow test was used. It tests the adequacy of the logistic regression model by examining the overall goodness- of-fit test. The model fits if the difference between the observed and the fitted values are small and there is no systematic contribution of the differences to the error structure of the model. The hypothesis of the Hosmer-Lemeshow goodness of fit test is given by H : The model fits the data 0 H : The model does not fit the data 1 72 University of Ghana http://ugspace.ug.edu.gh The null hypothesis is rejected if the p-value is less than 0.05 and conclude that the model is not fit otherwise we fail to reject the null hypothesis and conclude that the model is fit for the observed data. Table 4.14: Hosmer and Lemeshow Test Chi- Step df Sig. square 1 11.702 8 0.165 Since the p-value in our test is greater than 0.05, we fail to reject the null hypothesis that the model is fit. Hence, we conclude that the logistic model is fit for the observed data. The factors impacting the adoption of internet banking is not significantly different from those used in the model. 73 University of Ghana http://ugspace.ug.edu.gh Table 4.15: Variables in the Equation 95% C.I.for EXP(B) B S.E. Wald df Sig. Exp(B) Lower Upper Age .254 .336 .570 1 .450 1.289 .667 2.488 Educational Level .644 .221 8.484 1 .004 1.905 1.235 2.938 Occupation .053 .179 .088 1 .767 1.055 .743 1.498 -1.322 .309 18.348 1 .000 .267 .146 .488 Trustworthiness .207 .264 .613 1 .434 1.230 .733 2.064 Usefulness .351 .327 1.154 1 .283 1.421 .749 2.696 Risk .950 .313 9.244 1 .002 2.586 1.402 4.772 Accessibility 1.266 .250 25.634 1 .000 3.546 2.172 5.789 Ease of Use Assurance of -.869 .313 7.718 1 .005 .419 .227 .774 Website .952 .299 10.128 1 .001 2.590 1.441 4.653 Service Visibility Knowledge of the 1.123 .252 19.940 1 .000 3.075 1.878 5.036 product -.267 .270 .985 1 .321 .765 .451 1.298 Tust in the product Constant .971 1.018 .909 1 .340 2.640 The coefficients of the model predictors are tested via the hypothesis H0 :  j  0 H1 :  j  0 From Table 4.15, Educational Level, Trustworthiness, Accessibility, Ease of use, Assurance of Website, Service visibility, and knowledge of the product are significant at   0.05 with their respective significant values of 0.004, 0.000, 0.002, 0.000, 0.005, 0.001 and 0.000. Hence we reject the null hypothesis and conclude that they are each significant in predicting the adoption of internet banking using the binary logistic regression. 74 University of Ghana http://ugspace.ug.edu.gh However, Age, Occupation, Usefulness, Risk, and Trust in the product were not significant at   0.05 with respective significance level of 0.45, 0.767, 0.434, 0.351, and 0.321, hence we fail to reject the null hypothesis and conclude that they are each not significant in predicting the adoption of internet banking adoption using the binary logistic regression. The logistic regression model obtained from the model is follows log it  p  y 1  0.646Educationallevel 1.322Trustwothiness  0.950Accessibility 1.266Easeofuse0.869Assurance 0.952ServiceVissibility 1.123knowledgeofPropduct Also, from the table 4.15, the strongest factor in adopting internet banking is “Ease of Use”. This indicates how user friendly the internet banking platforms are. It had the odds ratio of 3.546(95% C.I=2.172-5.789). This indicates that banks with internet banking application that easy to use are 3.546 times more likely to influence the adoption of internet banking than those with application that are not easy to use controlling for all other factors in the model. The next highest was “knowledge of Product” with the odds ratio given by 3.075(95% C.I=1.878-5.036). This implead clients with the knowledge of the benefits of internet banking are 3.075 times more likely to adopt internet banking than those who did not have knowledge of the product controlling for all other factors in the model. “Service Visibility” had the next highest odds ratio given by 2.590 (95% C.I=1.441-4.653). This implies that banks with whose internet banking products are highlighted or visible have 2.59 times chance of influencing clients to adopt internet banking than those whose internet banking products are not controlling for all other factors in the model. The odds ratio of “Accessibility” of the internet banking applications was the next highest with 2.586 (95% C.I= 1.402-4.772). This indicates that banks with internet banking application that are accessible have 2.586 times chance of influencing clients to adopt their 75 University of Ghana http://ugspace.ug.edu.gh internet banking platforms than those whose applications are not easily accessible controlling for all other factors in the model. It can also be seen from Table 4.13 that the odds ratio of Education Level was 1.905 (95% C.I=1.235-2.938). This indicates that clients whose educational level are higher have 1.905 chance of adopting internet banking compared to clients with lower educational background controlling for all other factors in the model. The odds ratio for “Assurance” in the internet website was 0.419(95%C.I=0.227-0.774). This indicates that there were 41.9% decrease in adoption of internet banking adoption if there was lack of structural assurance in the internet banking website of the bank controlling for other variables in the model. Finally, the odds ratio of “Trustworthiness” was 0.267 (95% C.I=0.146-0.488). This indicates that there were 26.7% decrease in adoption of internet banking adoption if there was lack of Trustworthiness in the bank offering the internet banking controlling for all other factors in the model. For a unit increase in lack of trustworthiness, internet banking adoption will decrease by 0.927 controlling for all other factors in the model. 4.4 Internet Banking Adoption of Locally and Foreign Owned Banks To compare the adoption of internet banking of clients of locally owned banks and that of foreign owned banks, the factors where tested for multivariate normality using the Royston normality test. The hypothesis tested was H : The factors are multivariate normally distributed 0 H : The Factors are not multivariate normally distributed 1 76 University of Ghana http://ugspace.ug.edu.gh We reject the null hypothesis if the p-value is less than 0.05. Table 4.16: Multivariate Test for Normality H-test 4319.481 P-value 0.000 Since the p-value is less than 0.05, we reject the null hypothesis and conclude that the factors are not multivariate normally distributed. Since the assumption of multivariate normality was not met, the non-parametric multivariate analysis of variance was used to compare the means of the factors extracted. A modification to the F test statistic was used to compare the means of the factors influencing the adoption of internet banking. A distribution of the test statistic is created under the null hypothesis using permutation of the observation as suggested by Edgignton (1995). A 1000 permutation of the observations were carried out and the result of the F statistic generated. The hypothesis for the statistic is H : Internet Banking Adoption for Local and Foreign Banks are the same. 0 H : Internet Banking Adoption for Local and Foreign Banks are not the same. 1 We reject the null hypothesis if the p value is less than 0.05 Table 4.17: Test of average adoption for Local and Foreign Owned Banks Test P-value Test Statistic df1 df2 P- Permutation value F 1.4665 7 392 0.178 0.173 77 University of Ghana http://ugspace.ug.edu.gh Since the P-value of the F statistic is greater than 0.05 we fail to reject the null hypothesis and conclude that there is no enough evidence to say that the means of factors affecting adoption internet banking for locally and foreign owned banks are not the same. 78 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE CONCLUSION AND RECOMMENDATION 5.1 Introduction The study sought to analyse the factors affecting the “internet banking adoption” in Ghana from the customer’s perspective. Response of clients of commercial banks in Ghana were analysed using the factor analysis and the binary logistic regression. The summary results, conclusion and recommendation of the study are presented in this Chapter. 5.2 Summary The factors influencing the adoption of internet banking in Ghana were analysed using Principal Axis method of Factor Analysis and Oblimin rotation. It was realized that nine factor were significant. These factors are “Trustworthiness”, “Usefulness”, “Risk”, “Accessibility”, “Ease of Use” “Assurance in Banks Website”, “Service Visibility”, “Awareness of the Benefits of Internet Banking” and “Trust in Internet Banking as a solution to the banking needs of clients. Additionally, chi square test of association showed a relationship between age, educational level and occupation clients of commercial banks. To determine the chance of a customer adopting internet banking given a significant factor, the binary logistic regression was used. The binary logistic regression indicated that Educational level, Trustworthiness, Accessibility, Ease of use, Assurance Service visibility and knowledge of the product were significant in predicting internet banking adoption. 79 University of Ghana http://ugspace.ug.edu.gh Whiles age, Occupation, Usefulness, Risk and Trust in the Product were not significant in predicting internet banking adoption. It was noted that “Ease of Use” had the highest chance of influencing clients of commercial banks to adopt internet banking with 3.546 times more likely to influence clients at 95% confidence level controlling for all other factors in the model. This was followed closely by “Knowledge of Product” with 3.075 times more likely to influence clients of commercial banks to adopt internet banking controlling for all other factors in the model. “Service Visibility” and “Accessibility” were 2.59 and 2.586 times more likely to influence clients of commercial banks to adopt internet banking respectively controlling for all other factors in the model. Also, “Educational Level” was found to be 1.905 times more likely to influence clients of commercial banks controlling for all other factors in the model. However, lack of “Assurance” in the internet banking platform and lack of “Trustworthiness” respectively, were found to decrease internet banking adoption by 41.90% and 26.7% controlling for all other factors in the model. Also, to compare the adoption of internet banking of clients of locally owned and foreign owned banks, a modification of the F test as suggested by Edgignton (1995) was used to compare the mean adoption internet banking of clients of locally and foreign owned banks in Ghana. The test suggested there was no difference between the adoptions of internet banking of client of locally and foreign owned banks. 80 University of Ghana http://ugspace.ug.edu.gh 5.3 Conclusion The objective of this study was to investigate the factors that influence the adoption of internet banking in Ghana, determine the chance of a customer adopting internet banking given a particular factor and compare the internet banking adoption of clients of locally and foreign owned banks. The study found that nine factors were significant in influencing clients of commercial banks in Ghana to adopt internet banking using the exploratory Factor Analysis. These are Trustworthiness, Accessibility, Ease of Use, Assurance, Service Visibility, and Product knowledge, Usefulness, Risk and Trust in the product. The study also found that Ease of Use, Product Knowledge, Service Visibility, Accessibility, Educational level respectively were 3.546, 3.075, 2.59, 2.586 and 1.905 more likely to influence internet banking adoption of clients of commercial banks in Ghana controlling for all other factors. Additionally, the study found that lack of Trustworthiness and Assurance decrease the adoption of internet banking by 26.7% and 41.9% respectively controlling for all other factors. Finally, the study found that there was not difference in the adoption of internet banking of clients of locally and foreign owned commercial banks in Ghana. 5.4 Recommendations We recommend that commercial banks in Ghana should take a keen interest in making the internet banking platforms easy to use or user friendly since this impacts more on client’s adoption of the product. 81 University of Ghana http://ugspace.ug.edu.gh Additionally the more clients are aware of the internet banking products and its benefits the more they are likely to adopt this product as suggested by this study. Hence banks should make it a point of educating its clients and the general public about the internet banking product and its benefits. Also, banks should make their internet banking platforms readily available as frequently breakdown of platforms can influence the adoption of the internet banking products of the bank. 5.5 Limitation of the study In carrying out the study there were some limitations that are likely to have impact on the study. The study could not get the total number of clients of each bank in order to estimate the number clients of each bank to administer the questionnaire. The share of deposits of the respective banks where used in allocating the number of clients of the banks to administer questionnaire to. In cases where the share of deposits of banks are not proportional to the number of clients of the banks, this assumption could be flawed. Also, the use of purposive sampling techniques in the study could influence the study as a result of the personal bias of the investigator in selecting the banks for the study. There is no possibility of knowing extent of accuracy achieved when this method is used and also the sampling error cannot be estimated. Additionally, the perspective of clients of other banks that were not sampled, on internet banking as a result of the choice purposive sampling will not be captured on this study. Also, the use of accidental sampling could also lead to bias in sampling the clients. Cooperate clients and other clients who do not come to the banking hall for transaction would 82 University of Ghana http://ugspace.ug.edu.gh automatically be left out in the study. Additionally, clients who make use of internet banking properly would be left out as they do not need to come to the banking hall at all. 83 University of Ghana http://ugspace.ug.edu.gh REFERENCES “Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision sciences, 30(2), 361-391. Al-Rfou, R., Perozzi, B., & Skiena, S. (2013). 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International Journal of Bank Marketing, 17(7), 324-334. 86 University of Ghana http://ugspace.ug.edu.gh Suzanne Harrison, T., Peter Onyia, O., & K. Tagg, S. (2014). Towards a universal model of internet banking adoption: initial conceptualization. International Journal of Bank Marketing, 32(7), 647-687. Taylor, S. A., & Baker, T. L. (1994). An assessment of the relationship between service quality and customer satisfaction in the formation of consumers' purchase intentions. Journal of retailing, 70(2), 163-178. Thulani, D. T., Tofara, C. C., & Longton, R.(2009): Adoption and use of Internet Banking in Zimbabwe: An Exploratory study. Journal of Internet Banking and Commerce, 14(1), 1-13. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154. Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799-822. Yee-Loong Chong, A., Ooi, K.-B., Lin, B., & Tan, B.-I. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267-287." 87 University of Ghana http://ugspace.ug.edu.gh “APPENDICES Appendix A: Questionnaire UNIVERSITY OF GHANA DEPARTMENT OF STATISTICS “Topic: Statistical Analysis of Factors Influencing Internet Banking Adoption in Ghana: The Customer Perspective This Questionnaire is designed to seek empirical data for the conduct of the above topic which is purely an academic exercise. This will be submitted for the partial fulfilment of Masters of Philosophy (MPhil) in Statistics. Your support and co-operation is very much anticipated and your responses will be treated with maximum confidentiality. INSTRUCTION: Please tick/mark [√] the appropriate response to each question NOTE: For the purpose of this questionnaire, “Internet banking” describes the various banking products that make use of the internet. This includes checking balances, paying bills, transferring fund. SECTION A 1. Sex a. Male [ ] b. Female [ ] 2. What is your age? a. Less than 20 Years [ ] b. 20-30 Years [ ] c. 31-40 Years [ ] d. 41-50 Years [ ] e. 51 years and above [ ] 3. What is your highest educational qualification? a. JHS [ ] b. SHS [ ] c. Diploma [ ] d. Bachelor [ ] e. Masters [ ] f. PhD [ ] g. Professional [ ] 4. What is your current occupation? a. Student [ ] b. Public servant [ ] c. Civil servant [ ] d. Private/Entrepreneur [ ] e. Housewife [ ] f. Pensioner [ ] g. Other ………………………………. 5. Name of Bank ………. 6. Do you use internet banking? a. Yes [ ] b. No [ ] 88 University of Ghana http://ugspace.ug.edu.gh SECTION B 1. How long have you used Internet banking? a. Less than one year [ ] b. 1-2 years [ ] c. More than 2 years year [ ] 2. Which of the following mode of transactions do you use frequently? 1. Branch banking [ ] 2. Cash Machine [ ] 3. Phone Banking [ ] 4. Internet Banking [ ] 3. Which banking services do you always use through Internet banking? (Please check all that apply) a. Basic account information [ ] b. Making online bill payments [ ] c. Accounting check balance [ ] d. Bank transfer [ ] e. Inter-account transfer [ ] f. Stock trading [ ] h. Applying for cheque books [ ] i. Other …………………………………… Please Tick the number which best reflects your level of agreement or disagreement with the following statements. [Where 1 = Strongly Disagree; 2 = Disagree; 3 = Not sure; 4 = Agree; 5 = Strongly Agree] 1 2 3 4 5 1 “Internet banking” enables clients to conduct banking transactions more quickly. 2 “Internet banking” enables clients to conduct banking transactions anytime. 3 “Internet banking” makes it easier for clients to conduct banking transactions 4 “Internet banking” enables me to manage my bank account (s) more effectively. 5 “Internet banking” is very useful in conducting clients banking transactions. 6 “Internet banking” is easier to learn and become skilful. 7 It easy for client to learn how to use Internet banking to conduct banking transactions. 8 Internet banking is very easy to use. 9 Using internet banking does not demand more mental effort. 10 Using “internet banking” website is understandable and clearer 11 I intend to use the service for doing some of my banking transactions 89 University of Ghana http://ugspace.ug.edu.gh 12 I intend to use the service for most of my banking transactions 13 The service will be more useful for some of my banking transactions. 14 The service will be more useful for most of my banking transactions. 1 2 3 4 5 15 My bank can be trusted as an internet service provider 16 My decision on the use of internet banking is because it is a trusted medium of financial transactions. 17 Overall, I trust Internet banking to perform my banking transactions. 18 The decision towards the use of Internet banking to conduct banking transactions is significant risky 19 The decision towards the use of the service to conduct banking transactions can lead to potential for loss 20 The decision towards the use of the service to conduct banking transactions is a not a good decision 21 The legal structures of my bank guard client from problems. 22 Technological structures of my Bank enable clients to carry out transactions safely. 23 The platform used by my bank for the service is adequately protected to enable me carry out transaction. 24 The platform used by my bank for the service is robust and safe 25 The bank is competent in providing excellent service using the product. 26 My bank has the capability to meet its Internet banking customers’ needs. 27 The bank knows how to provide excellent Internet banking services 28 My bank is one of the best banks in proving the service. 29 The bank is honest to its clients enrolled to “internet Banking”. 30 In dealing with the service you can trust my bank 31 My bank keeps their word when it come to the service 32 The bank is acting in my best interest. 33 When it comes to the service, my bank is always ready to assist 90 University of Ghana http://ugspace.ug.edu.gh 34 When it comes to the service, my bank has good intention to assist their clients. 35 My bank keeps adequate information about service transactions. 36 Right levels of information regarding the service transaction are kept. 37 My bank maintains accurate information relating to my bank needs. 38 Details of my accounts always are accurate anywhere 39 Details of my accounts are current. 40 The information about my accounts maintained by my bank is enough. 41 My banks makes locating the service easy 42 The service is provides me details of the products offered by my bank. 43 The service provides exact meaning of the products. 44 The processes for conducting each of the service is made clear 45 The services is displayed in an obvious form 46 I like the form the service is provided 47 The service is provided in a form that makes it easy to be used. 48 The process of carrying out the service is confusing 49 Denials from the system when carrying out the transaction is frequent. 50 I do rely on the system to come up when needed 51 I believe that if I required assistance in accessing a banking service, my bank would assist me on that. 52 My banks assist me when I encounter a challenge in using the service. 53 When I experience fraud, my bank assist me 54 With the service, I get quicker and easier service 55 The service is always available 56 It is easy for me to get access any banking service that I need to conduct 91 University of Ghana http://ugspace.ug.edu.gh 57 Specific transactions that require a phone call for completion (such as beneficiary identification) are not time consuming. 58 Overall task technology of the bank fits properly 92 University of Ghana http://ugspace.ug.edu.gh Appendix B: Total Variance Explained Rotation Extraction Sums of Squared Sums of Factor Initial Eigenvalues Loadings Squared Loadingsb % of Cumulative % of Cumulative Total Total Total Variance % Variance % 1 25.989 44.809 44.809 25.706 44.320 44.320 15.295 2 5.127 8.839 53.648 4.826 8.320 52.641 13.083 3 2.560 4.413 58.062 2.188 3.773 56.414 5.348 4 2.325 4.008 62.070 2.061 3.553 59.967 13.793 5 1.778 3.065 65.135 1.491 2.570 62.537 6.554 6 1.605 2.767 67.903 1.315 2.266 64.803 11.602 7 1.365 2.353 70.256 1.081 1.864 66.667 14.390 8 1.273 2.196 72.451 .982 1.693 68.360 4.385 9 1.139 1.964 74.415 .810 1.396 69.756 4.778 10 1.024 1.766 76.181 11 .964 1.663 77.843 12 .913 1.574 79.418 13 .782 1.348 80.766 14 .742 1.280 82.046 15 .702 1.210 83.256 16 .687 1.185 84.441 17 .608 1.048 85.488 18 .580 .999 86.488 19 .569 .982 87.470 20 .518 .894 88.363 21 .506 .873 89.236 22 .464 .799 90.035 23 .433 .746 90.781 24 .426 .735 91.516 25 .365 .630 92.146 26 .355 .612 92.758 27 .319 .550 93.308 28 .305 .526 93.834 29 .287 .495 94.329 30 .267 .461 94.790 31 .258 .445 95.235 32 .243 .420 95.654 33 .219 .378 96.032 34 .207 .356 96.388 35 .185 .320 96.708 36 .182 .313 97.021 37 .166 .286 97.308 93 University of Ghana http://ugspace.ug.edu.gh 38 .164 .282 97.590 39 .151 .260 97.849 40 .137 .236 98.086 41 .126 .217 98.303 42 .119 .205 98.507 43 .108 .186 98.693 44 .102 .177 98.870 45 .081 .140 99.010 46 .070 .120 99.130 47 .067 .116 99.246 48 .065 .113 99.359 49 .061 .105 99.464 50 .056 .097 99.561 51 .048 .083 99.644 52 .045 .078 99.722 53 .040 .069 99.791 54 .033 .057 99.848 55 .031 .054 99.901 56 .022 .038 99.939 57 .019 .033 99.972 58 .016 .028 100.000 Extraction Method: Principal Axis Factoring. a. Only cases for which Do you use internet banking = Yes are used in the analysis phase. b. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance. 94 University of Ghana http://ugspace.ug.edu.gh Appendix C: Correlation Matrix Variable 1 2 3 4 5 6 7 Correlation 1 1.000 .674 .657 .579 .690 .593 .668 2 .674 1.000 .606 .582 .648 .471 .530 3 .657 .606 1.000 .544 .658 .496 .511 4 .579 .582 .544 1.000 .666 .515 .610 5 .690 .648 .658 .666 1.000 .583 .563 6 .593 .471 .496 .515 .583 1.000 .755 7 .668 .530 .511 .610 .563 .755 1.000 8 .559 .597 .509 .598 .571 .619 .769 9 .567 .524 .435 .553 .560 .632 .728 10 .500 .470 .441 .478 .481 .640 .690 11 .622 .541 .446 .568 .569 .550 .632 12 .685 .595 .524 .483 .643 .529 .611 13 .543 .506 .446 .470 .550 .461 .553 14 .626 .621 .557 .492 .616 .511 .593 15 .547 .521 .467 .473 .577 .510 .625 16 .577 .582 .433 .493 .618 .555 .540 17 .505 .419 .335 .372 .503 .475 .515 18 .333 .156 .162 .181 .233 .370 .354 19 .234 .146 .138 .095 .188 .264 .230 20 .087 -.005 .041 .047 .009 .148 .086 21 .297 .384 .123 .248 .287 .342 .358 22 .356 .405 .276 .380 .329 .377 .430 23 .427 .362 .253 .290 .332 .387 .450 24 .329 .425 .204 .360 .355 .294 .404 25 .456 .421 .408 .348 .394 .453 .509 26 .567 .447 .460 .431 .447 .409 .591 27 .448 .357 .380 .337 .408 .411 .476 28 .483 .441 .447 .353 .475 .431 .510 29 .539 .550 .402 .513 .489 .478 .595 30 .509 .436 .462 .464 .562 .607 .568 31 .591 .453 .466 .447 .475 .477 .598 32 .487 .467 .529 .330 .510 .373 .463 33 .530 .433 .385 .408 .547 .474 .553 34 .455 .403 .344 .322 .388 .404 .472 35 .505 .398 .467 .319 .516 .471 .538 36 .400 .392 .369 .352 .469 .441 .491 37 .424 .332 .362 .276 .382 .427 .393 38 .497 .421 .489 .462 .488 .365 .405 39 .511 .529 .461 .332 .419 .257 .385 40 .584 .595 .539 .353 .523 .362 .469 95 University of Ghana http://ugspace.ug.edu.gh 8 9 10 11 12 13 14 15 1 .559 .567 .500 .622 .685 .543 .626 .547 2 .597 .524 .470 .541 .595 .506 .621 .521 3 .509 .435 .441 .446 .524 .446 .557 .467 4 .598 .553 .478 .568 .483 .470 .492 .473 5 .571 .560 .481 .569 .643 .550 .616 .577 6 .619 .632 .640 .550 .529 .461 .511 .510 7 .769 .728 .690 .632 .611 .553 .593 .625 8 1.000 .704 .697 .622 .622 .562 .626 .540 9 .704 1.000 .705 .635 .577 .579 .562 .464 10 .697 .705 1.000 .522 .576 .538 .570 .481 11 .622 .635 .522 1.000 .723 .765 .635 .445 12 .622 .577 .576 .723 1.000 .662 .791 .628 13 .562 .579 .538 .765 .662 1.000 .560 .526 14 .626 .562 .570 .635 .791 .560 1.000 .470 15 .540 .464 .481 .445 .628 .526 .470 1.000 16 .435 .543 .447 .511 .553 .477 .503 .659 17 .472 .543 .511 .467 .568 .400 .484 .625 18 .216 .543 .221 .261 .248 .346 .127 .345 19 .076 .543 .134 .069 .148 .215 .151 .320 20 -.083 .543 -.008 -.013 .011 .046 -.036 .131 21 .288 .543 .218 .408 .345 .312 .336 .356 22 .325 .543 .315 .384 .408 .275 .400 .469 23 .387 .543 .400 .366 .405 .276 .434 .489 24 .295 .543 .311 .319 .393 .303 .389 .464 25 .470 .543 .525 .505 .533 .356 .544 .406 26 .470 .543 .577 .485 .514 .389 .559 .457 27 .432 .543 .456 .331 .441 .320 .449 .490 28 .500 .543 .501 .351 .476 .336 .520 .438 29 .615 .543 .626 .600 .586 .474 .576 .397 30 .537 .543 .609 .552 .547 .516 .522 .522 31 .455 .543 .558 .521 .517 .408 .520 .423 32 .403 .543 .476 .368 .394 .322 .379 .472 33 .528 .543 .572 .599 .614 .587 .569 .453 34 .383 .543 .494 .443 .494 .383 .494 .300 35 .469 .543 .570 .431 .530 .487 .454 .490 36 .416 .543 .476 .385 .492 .452 .532 .468 37 .361 .543 .474 .361 .440 .344 .475 .380 38 .394 .543 .336 .495 .525 .422 .487 .535 39 .419 .543 .277 .393 .533 .484 .518 .571 40 .511 .543 .343 .426 .499 .476 .532 .573 96 University of Ghana http://ugspace.ug.edu.gh 16 17 18 19 20 21 22 23 1 .577 .505 .333 .234 .087 .297 .356 .427 2 .582 .419 .156 .146 -.005 .384 .405 .362 3 .433 .335 .162 .138 .041 .123 .276 .253 4 .493 .372 .181 .095 .047 .248 .380 .290 5 .618 .503 .233 .188 .009 .287 .329 .332 6 .555 .475 .370 .264 .148 .342 .377 .387 7 .540 .515 .354 .230 .086 .358 .430 .450 8 .435 .472 .216 .076 -.083 .288 .325 .387 9 .543 .466 .243 .159 .105 .393 .395 .471 10 .447 .511 .221 .134 -.008 .218 .315 .400 11 .511 .467 .261 .069 -.013 .408 .384 .366 12 .553 .568 .248 .148 .011 .345 .408 .405 13 .477 .400 .346 .215 .046 .312 .275 .276 14 .503 .484 .127 .151 -.036 .336 .400 .434 15 .659 .625 .345 .320 .131 .356 .469 .489 16 1.000 .614 .211 .283 .263 .455 .539 .486 17 .614 1.000 .455 .315 .123 .329 .423 .537 18 .211 .455 1.000 .715 .359 .227 .098 .137 19 .283 .315 .715 1.000 .432 .266 .280 .265 20 .263 .123 .359 .432 1.000 .402 .200 .210 21 .455 .329 .227 .266 .402 1.000 .559 .531 22 .539 .423 .098 .280 .200 .559 1.000 .754 23 .486 .537 .137 .265 .210 .531 .754 1.000 24 .570 .484 .176 .278 .286 .589 .724 .774 25 .453 .546 .155 .165 .082 .435 .644 .691 26 .516 .513 .215 .186 .185 .389 .513 .584 27 .487 .493 .157 .142 .207 .340 .523 .591 28 .574 .481 .211 .255 .206 .333 .534 .546 29 .489 .484 .165 .154 .051 .492 .523 .601 30 .508 .469 .235 .314 .085 .385 .562 .574 31 .484 .459 .172 .222 .181 .405 .612 .626 32 .480 .478 .188 .250 .220 .286 .475 .553 33 .443 .463 .267 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.376 .487 .507 .556 .465 .302 .494 40 .342 .274 .481 .379 .459 .518 .639 .591 .500 .332 .593 101 University of Ghana http://ugspace.ug.edu.gh 1 2 3 4 5 6 7 41 .439 .431 .252 .313 .300 .341 .511 42 .262 .211 .222 .054 .171 .172 .259 43 .389 .285 .179 .319 .344 .395 .456 44 .249 .168 .169 .168 .255 .245 .364 45 .282 .178 .233 .204 .268 .237 .350 46 .284 .226 .211 .168 .221 .193 .276 47 .358 .322 .252 .220 .357 .360 .399 48 .287 .279 .229 .374 .287 .365 .464 49 .276 .207 .153 .279 .179 .242 .269 50 .269 .298 .257 .221 .263 .252 .364 51 .242 .221 .137 .253 .233 .205 .362 52 .327 .187 .301 .228 .228 .253 .353 53 .304 .276 .199 .199 .241 .210 .291 54 .390 .371 .311 .271 .310 .187 .355 55 .365 .366 .285 .213 .311 .284 .436 56 .374 .309 .251 .220 .250 .249 .418 57 .116 .240 -.007 .238 .138 .130 .287 58 .382 .448 .368 .301 .414 .298 .453 8 9 10 11 12 13 14 15 41 .459 .543 .373 .335 .371 .355 .351 .415 42 .246 .543 .272 .233 .213 .177 .233 .137 43 .364 .543 .388 .410 .437 .285 .324 .496 44 .326 .543 .345 .311 .295 .243 .292 .336 45 .317 .543 .275 .243 .231 .185 .325 .282 46 .342 .543 .383 .284 .319 .225 .387 .229 47 .282 .543 .288 .423 .353 .348 .414 .348 48 .349 .543 .417 .408 .355 .334 .288 .335 49 .207 .543 .103 .274 .214 .213 .161 .233 50 .446 .543 .408 .317 .285 .330 .261 .350 51 .289 .543 .300 .234 .225 .195 .283 .246 52 .324 .543 .284 .232 .225 .218 .305 .198 53 .267 .543 .203 .193 .246 .200 .214 .364 54 .323 .543 .348 .277 .306 .309 .347 .373 55 .413 .543 .421 .235 .332 .285 .415 .365 56 .392 .543 .409 .210 .285 .218 .332 .339 57 .226 .543 .225 .153 .144 .178 .270 .139 58 .443 .543 .434 .306 .404 .355 .451 .384 102 University of Ghana http://ugspace.ug.edu.gh 16 17 18 19 20 21 22 23 41 .398 .376 .228 .169 .222 .275 .372 .474 42 .236 .269 -.009 .114 .052 .214 .428 .484 43 .467 .468 .170 .197 .074 .294 .623 .491 44 .277 .405 .171 .219 .189 .250 .440 .503 45 .397 .327 .091 .238 .175 .254 .546 .475 46 .304 .357 .038 .110 .148 .257 .412 .495 47 .600 .425 .125 .239 .290 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.562 .556 .625 .479 .457 .492 54 .306 .465 .495 .516 .505 .438 .393 .441 55 .386 .467 .567 .501 .575 .486 .360 .443 56 .360 .517 .638 .575 .610 .509 .457 .450 57 .344 .457 .582 .567 .580 .461 .394 .450 58 .494 .374 .339 .296 .383 .435 .300 .406 59 .501 .605 .579 .583 .606 .559 .505 .501 103 University of Ghana http://ugspace.ug.edu.gh 32 33 34 35 36 37 38 39 41 .479 .377 .444 .453 .410 .393 .400 .514 42 .432 .379 .525 .494 .413 .587 .447 .291 43 .515 .491 .521 .487 .449 .490 .413 .318 44 .536 .492 .438 .490 .468 .502 .397 .322 45 .539 .421 .465 .395 .487 .475 .390 .343 46 .528 .475 .540 .485 .515 .558 .513 .395 47 .485 .469 .481 .457 .515 .460 .392 .346 48 .362 .350 .471 .424 .348 .356 .325 .324 49 .164 .192 .196 .203 .118 .131 .192 .298 50 .531 .476 .436 .569 .412 .462 .434 .429 51 .584 .316 .613 .386 .452 .486 .393 .349 52 .524 .320 .518 .465 .458 .481 .411 .376 53 .521 .292 .413 .505 .464 .402 .461 .487 54 .552 .305 .471 .510 .422 .362 .404 .507 55 .521 .391 .475 .607 .519 .445 .403 .556 56 .489 .274 .430 .536 .423 .416 .335 .465 57 .287 .271 .480 .298 .360 .405 .283 .302 58 .565 .387 .523 .601 .545 .512 .456 .494 40 41 42 43 44 45 46 47 41 .565 1.000 .522 .492 .547 .504 .502 .505 42 .390 .522 1.000 .516 .619 .576 .619 .500 43 .391 .492 .516 1.000 .666 .654 .574 .546 44 .413 .547 .619 .666 1.000 .720 .725 .693 45 .526 .504 .576 .654 .720 1.000 .750 .704 46 .469 .502 .619 .574 .725 .750 1.000 .630 47 .472 .505 .500 .546 .693 .704 .630 1.000 48 .342 .455 .336 .447 .514 .504 .417 .658 49 .274 .405 .208 .416 .263 .290 .286 .291 50 .481 .402 .422 .408 .499 .460 .488 .444 51 .379 .562 .506 .596 .531 .660 .597 .485 52 .459 .537 .558 .434 .656 .681 .702 .581 53 .518 .575 .421 .538 .568 .581 .604 .502 54 .639 .569 .493 .421 .579 .599 .628 .519 55 .591 .559 .458 .397 .610 .499 .545 .521 56 .500 .547 .399 .409 .556 .495 .492 .488 57 .332 .422 .364 .358 .387 .528 .437 .463 58 .593 .583 .588 .436 .598 .563 .591 .628 104 University of Ghana http://ugspace.ug.edu.gh 48 49 50 51 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.578 1.000 .658 58 .515 .207 .607 .625 .665 .598 .670 .775 .770 .658 1.000 105 University of Ghana http://ugspace.ug.edu.gh Appendix D List of Banks No Foreign Local 1 Access Bank ADB Bank 2 BSIC Ghana Ltd CAL Bank 3 Sovereign Bank Fidelity Bank 4 Standared Chartered Bank First Atlantic Bank 5 Barclays Bank GCB Bank 6 Soceite General GN Bank 7 Stanbic Bank HFC Bank 8 Bank of Africa National Investment Bank 9 FBN Bank Prudential Bank 10 Ecobank The Royal Bank 11 First National Bank(FNB) Unibank 12 Bank of Baroda Universal Merchant Bank 13 Energy Bank UT Bank 14 Guaranty Trust Bank Capital Bank 15 United Bank for Africa 16 Zenith Bank “ 106