University of Ghana http://ugspace.ug.edu.gh UNIVERSITY OF GHANA EXAMINING THE FACTORS THAT INFLUENCE THE ADOPTION OF MOBILE MONEY IN GHANA BY DORCAS ASANOA TANDOH (10396015) THIS THESIS/DISSERATION IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF MPHIL OPERATION & MANAGEMENT INFORMATION SYSTEM DEGREE JULY 2016 i University of Ghana http://ugspace.ug.edu.gh DECLARATION I do hereby declare that this work is the results of my own research and has not been presented by anyone for any academic award in this or any university. All references used in the work have been fully acknowledged. I bear sole responsibility for any shortcomings. …………………………………………. ……….………………… DORCAS ASANOA TANDOH DATE STUDENT ii University of Ghana http://ugspace.ug.edu.gh CERTIFICATION I hereby certify that this was supervised in accordance with procedures laid down by the University. ………………………………………… ……………………… PROF. RICHARD BOATENG DATE (SUPERVISOR) ………………………………………… ………………………. DR. JOHN EFFAH DATE (CO-SUPERVISOR) iii University of Ghana http://ugspace.ug.edu.gh DEDICATION I dedicate this work to the Almighty God who has given me life, strength and wisdom to enable me to undertake this research. I also dedicate this work to my family who have been my number one support system throughout this process. iv University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENT My special appreciation is to my supervisor, Prof. Richard Boateng, for his support during the course of my study. I also extend my gratitude to my co-supervisor, Dr. John Effah, for his support. Many thanks also go to Dr. Erasmus Addae, and Mr. Prince Kwame Senyo of the OMIS Department. Finally, my gratitude goes to Mr. Francois Agble, Mrs. Mateko Okantey, Mr. Alfred Sekyere Mbroko, and Mr. Emmanuel Owusu, and all those who helped in collecting data and the people who agreed to participate in this study. v University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS UNIVERSITY OF GHANA ............................................................................................................ I DECLARATION ............................................................................................................................ II CERTIFICATION ........................................................................................................................ III DEDICATION .............................................................................................................................. IV ACKNOWLEDGEMENT ............................................................................................................. V LIST OF TABLES ........................................................................................................................ IX LIST OF FIGURES ....................................................................................................................... X ABSTRACT .................................................................................................................................. XI CHAPTER ONE ............................................................................................................................. 1 INTRODUCTION .......................................................................................................................... 1 1.1 BACKGROUND............................................................................................................................................... 1 1.2 RESEARCH PROBLEM .................................................................................................................................... 3 1.3 RESEARCH PURPOSE ..................................................................................................................................... 7 1.4 RESEARCH OBJECTIVES ................................................................................................................................ 7 1.5 RESEARCH QUESTIONS ................................................................................................................................. 7 1.6 SYNOPSIS OF THE CHAPTERS ........................................................................................................................ 8 CHAPTER TWO .......................................................................................................................... 10 LITERATURE REVIEW ............................................................................................................. 10 2.1 INTRODUCTION ..................................................................................................................................... 10 2.2 MOBILE MONEY -AN OVERVIEW ................................................................................................................ 11 2.3 DEFINITION OF MOBILE MONEY ................................................................................................................. 12 2.4 THE ADOPTION OF MOBILE MONEY ............................................................................................................ 13 2.5 MOBILE MONEY ECOSYSTEM ..................................................................................................................... 14 2.6 MOBILE MONEY TRANSFER PROCESS ......................................................................................................... 17 2.7 THEORETICAL FRAMEWORK ....................................................................................................................... 18 2.7.1 Technological Acceptance Model (TAM) .................................................................................................. 19 2.7.2 Diffusion of Innovation (IDT) .................................................................................................................... 19 2.7.3 Unified Theory of Acceptance and Use of Technology (UTAUT) .............................................................. 20 2.8 CONCEPTUAL FRAMEWORK (UTAUT2) ..................................................................................................... 21 CHAPTER THREE ...................................................................................................................... 22 RESEARCH FRAMEWORK ....................................................................................................... 22 3.1 INTRODUCTION ..................................................................................................................................... 22 3.1 OVERVIEW OF UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY 2 (UTAUT 2) ..................... 22 3.2 HYPOTHESIS DEVELOPMENT ....................................................................................................................... 26 3.2.1 Performance Expectancy (PE) ................................................................................................................... 26 vi University of Ghana http://ugspace.ug.edu.gh 3.2.2 Effort Expectancy (EE) .............................................................................................................................. 26 3.2.3 Social Influence (SI) .................................................................................................................................. 27 3.2.4 Hedonic Motivation (HM) ......................................................................................................................... 27 3.2.5 Facilitating Conditions (FC) ..................................................................................................................... 28 3.2.6 Price Value (PV) ........................................................................................................................................ 29 3.2.7 Behavioural Intention (BI) ......................................................................................................................... 29 3.3 SUMMARY ................................................................................................................................................... 30 CHAPTER FOUR ......................................................................................................................... 31 RESEARCH METHODOLOGY.................................................................................................. 31 4.1 INTRODUCTION ..................................................................................................................................... 31 4.2 RESEARCH PARADIGM ................................................................................................................................ 31 4.2.1 Positivist Paradigm Chosen: Why Positivism? .......................................................................................... 33 4.3 RESEARCH APPROACH ................................................................................................................................ 35 4.3.1 Why the Quantitative Method of Research? .............................................................................................. 37 4.4 RESEARCH STRATEGY ................................................................................................................................. 38 4.4.1 Survey ........................................................................................................................................................ 41 4.5 SAMPLING TECHNIQUES .............................................................................................................................. 41 4.5.1 Data Types and Sources ............................................................................................................................ 43 4.6 RESEARCH DESIGN ...................................................................................................................................... 44 4.6.1 Explorative research .................................................................................................................................. 44 4.7 DATA COLLECTION METHODS .................................................................................................................... 45 4.8 DATA ANALYSIS ......................................................................................................................................... 46 4.8.1 Structural Equation Modelling (SEM) ....................................................................................................... 47 4.9 CONSTRUCTS’ MEASUREMENT ................................................................................................................... 49 4.10 QUALITY STANDARDS FOR RESEARCH: VALIDITY AND RELIABILITY ............................................................. 50 4.10.1 Reliability ................................................................................................................................................... 50 4.10.2 Validity....................................................................................................................................................... 50 4.11 SUMMARY ................................................................................................................................................... 51 CHAPTER FIVE .......................................................................................................................... 52 ANALYSIS AND FINDINGS ..................................................................................................... 52 5.1 INTRODUCTION ..................................................................................................................................... 52 5.2 DATA SCREENING ....................................................................................................................................... 53 5.3 SAMPLED CHARACTERISTICS ..................................................................................................................... 54 5.2.1 Demographic Profile of Respondents ........................................................................................................ 54 5.1 MEASUREMENT MODEL ASSESSMENT AND CONFIRMATORY FACTOR ANALYSIS (CFA) ............................ 54 5.2 MEASUREMENT MODEL .............................................................................................................................. 57 5.3 INSTRUMENT VALIDATION .......................................................................................................................... 58 5.4 ASSESSING THE STRUCTURAL MODEL ........................................................................................................ 60 5.5 STRUCTURAL EQUATIONAL MODELLING .................................................................................................... 63 CHAPTER SIX ............................................................................................................................. 64 DISCUSSIONS OF RESULTS .................................................................................................... 64 6.1 INTRODUCTION ..................................................................................................................................... 64 6.2 THE UTAUT 2 MODEL ............................................................................................................................... 64 6.2.1 Performance Expectancy ........................................................................................................................... 65 6.2.2 Effort Expectancy ....................................................................................................................................... 65 6.2.3 Social Influence ......................................................................................................................................... 66 vii University of Ghana http://ugspace.ug.edu.gh 6.2.4 Hedonic Motivation ................................................................................................................................... 66 6.2.5 Facilitating Conditions .............................................................................................................................. 67 6.2.6 Price Value ................................................................................................................................................ 67 6.2.7 Behavioural Intention ................................................................................................................................ 68 6.3 DETERMINANTS OF MOBILE MONEY ADOPTION ......................................................................................... 68 6.4 SUMMARY ................................................................................................................................................... 69 CHAPTER SEVEN ...................................................................................................................... 70 CONCLUSIONS, IMPLICATIONS AND RECOMMENDATIONS ......................................... 70 7.1 INTRODUCTION ..................................................................................................................................... 70 7.2 REVIEW OF PREVIOUS CHAPTERS ............................................................................................................... 70 7.3 IMPLICATION FOR RESEARCH ...................................................................................................................... 72 7.4 IMPLICATIONS FOR PRACTICE AND POLICY ................................................................................................. 73 7.5 RESEARCH LIMITATIONS ............................................................................................................................. 74 7.6 FUTURE RESEARCH ..................................................................................................................................... 75 7.7 RESEARCH CONCLUSION ............................................................................................................................. 75 REFERENCES ............................................................................................................................. 77 APPENDIX ................................................................................................................................... 92 COPY OF QUESTIONNAIRE..................................................................................................... 92 viii University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES TABLE 2. 1 PLAYERS IN THE MOBILE MONEY ECOSYSTEM .......................................................... 16 TABLE 5. 1 GOODNESS OF FIT INDICES FOR MEASUREMENT MODEL ......................................... 57 TABLE 5. 2 CONSTRUCT RELIABILITY RESULTS FOR THE POOLED DATA FILE ........................... 59 TABLE 5. 3 STRUCTURAL PATH ANALYSIS ................................................................................. 63 ix University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES FIGURE 2. 1 THE MOBILE MONEY ECOSYSTEM ....................................................................... 15 FIGURE 3. 1 RESEARCH MODEL ............................................................................................... 25 FIGURE 5. 1 MEASUREMENT MODEL FOR MOBILE MONEY USAGE ......................................... 56 FIGURE 5. 2 STRUCTURAL MODEL ........................................................................................... 62 x University of Ghana http://ugspace.ug.edu.gh ABSTRACT The purpose of this research is to examine the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation and price value on consumers’ behavioural intention to adopt mobile money in Ghana. Studies on mobile money in developing countries have so far focused more on issues relating to adoption, use, deployment and diffusion. The most dominant among these areas is studies on adoption. However, there still remain salient factors of mobile money adoption such as Hedonic motivation and price value that have not been adequately scrutinized by extant researchers. To address these gaps in knowledge, this study investigated some mobile money consumers in Ghana using the extended Unified Theory of Acceptance and Use of Technology (UTAUT 2) as the theoretical lens and the quantitative survey approach as the research methodology. The result of the study found positive support for the influence of performance expectancy, effort expectancy, hedonic motivation, price value and social influence on behavioural intention. However, the relationship between facilitating conditions and behavioural intention was not supported. In view of this, the study recommends that mobile money service providers should adequately invest in organising free trial services for potential adopter and help develop measures that will help educate potential customers on the benefits that can be derived from the usage of the technology. Finally, the study calls for future researchers to examing how moderators such as gender, age and educational level influence the adoption of mobile money in Ghana. xi University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE INTRODUCTION 1.1 Background Globally, ownership and usage of mobile devices continues to grow rapidly. Statistics from the Global System for Mobile Association (GSMA) revealed that half of the world’s population had at least one mobile subscription, totaling over 3.6 billion unique mobile subscribers by the end of 2014 and a global connection penetration at 97 percent (GSMA Report, 2015). According to the International Telecommunication Union report (2015), developing countries such as Nigeria, Egypt, South Africa, Morocco, Algeria, Congo DR, Kenya, Tanzania, Ethiopia and Ghana are among the top 10 African countries with the highest mobile-cellular subscription (ITU, 2015). In Ghana, mobile-cellular telephone subscription and mobile-cellular telephone subscription per 100 inhabitants increased from 1,695,000 to 30,360,771 and 8.14 to 114.82 respectively within a decade (ITU, 2015). This exponential growth in mobile ownership and subscription in such developing countries has given rise to the mobile money phenomenon. Mobile money can be defined as “relating to a mobile wallet, which refers to a digital repository of electronic money developed and implemented on mobile devices, allowing peer-to-peer transactions between mobile devices from users of the same service” (Diniz, Cernev, & Albuquerque, 2011). In the past, transferring money proved difficult especially to the rural areas. Money was transferred popularly through bank transfers, Western Union Transfer, post office, bus terminal deliveries or through a friend or relative. In recent years, mobile money services have spread across much of Africa, Asia, Latin America, Europe and the Middle East. The number of registered mobile money accounts grew to reach 299 million globally at the end of December 2014 (GSMA Report, 2015). 1 University of Ghana http://ugspace.ug.edu.gh Another GSMA Report (2015) suggests that, in three-quarters of the markets where mobile money is available, agent outlets outnumber bank branches. According to Chauhan (2015), the objective of mobile money is to enable person-to-person and person-to-merchant payments using mobile phones without the need to have bank accounts. Jenkins (2008) suggested that “mobile subscribers in markets such as South Africa, Kenya, the Philippines, Japan, and elsewhere are beginning to use mobile money for transactions and services including domestic and international remittances, bill payment, payroll deposit, loan receipt and repayment, and purchases of goods and services ranging from prepaid airtime to groceries to bus tickets to micro insurance”. The Telecommunication Industry in Ghana has taken advantage of this by incorporating more services apart from just the transfer of money to linking networks to bank accounts in order to pay utility bills, top-up airtime or data bundles, international money transfer, microloans and savings products, bank deposits and withdrawal. The presence of the mobile phone is providing opportunities and possibilities for improving the lives of millions (Chipchase & Lee, 2011), but the success of mobile money services can be achieved only if it is accepted by the citizens as the lack of acceptance may render such services to be fruitless (Chauhan, 2015). 2 University of Ghana http://ugspace.ug.edu.gh 1.2 Research Problem A world bank report indicates that although about 6 billion people are owners of mobile devices, over 2 billion adults still lack access to formal financial services (ITU, 2015; World Bank Annual Report, 2015). Mobile money is a rapidly maturing industry that is bringing financial inclusion to a growing number of previously unbanked and underbanked populations residing in developing regions (ITU, 2015). A Communications Commission of Kenya (CCK) report also suggests that M-Pesa in Kenya, has seen significant growth in its delivery of mobile money services with more than 19 million mobile money service users (CCK Report, 2012). Although mobile money has seen relevant increase in many developing countries across the world, its adoption is still quite low (Aker, Boumnijel, Tierney, & McClelland, 2013; Donovan, 2012). Researchers have outlined numerous benefits associated with this rapidly growing phenomenon but there are some disadvantages. Research shows that mobile money reduces implementation costs, transaction costs, simplifying or enabling transactions, alternative means of saving, making the financial sector more inclusive, privacy and autonomy, reliability, money and physical security, employment opportunities, social capital accumulation and business expansions (Aker et al., 2013; Donovan, 2012; William Jack & Suri, 2011; Castri, 2013; Tobbin, 2012; Plyler, Haas, Nagarajan, 2010). In contrast, cost of owning a mobile phone or device and also gaining access to agents proved to be a challenge in some areas (Aker et al., 2013). Notwithstanding, the benefits clearly outweigh the disadvantages in this instance therefore it is safe to add that understanding the reasons for adopting this service is a necessary contribution to existing literature which may reveal additional benefits or disadvantages of the mobile money service. 3 University of Ghana http://ugspace.ug.edu.gh In a review of literature, Diniz, Cernev, & Albuquerque (2011) acknowledged that the terms mobile money, mobile banking and mobile payments were used interchangeably and definitions were not quite clear. They defined mobile payments as “payments enabled through mobile technology, with or without the use of mobile telecommunications and not necessarily linked to a financial institution”, mobile banking as “banking services via a portable devices connected to telecommunications networks that provide users access to mobile payments, transactions and other banking and financial services linked to customer accounts, with or without the direct participation of traditional banking institutions”, and lastly mobile money as “relating to a mobile wallet, which refers to a digital repository of electronic money developed and implemented on mobile devices, allowing peer-to-peer transactions between mobile devices from users of the same service. Its equivalent is electronic money or mobile-cash and can be differentiated from other forms of electronic payments because of its ability to the essential attributes of traditional money such as liquidity, acceptability and anonymity” (Diniz et al., 2011). This paper therefore seeks to adopt the latter definition. Although extant studies exist on mobile money adoption in developing countries such as India and Pakistan (Chauhan, 2015; Donner & Tellez, 2008; Mukherjee, 2015; Olasina et al., 2015; Thakur & Srivastava, 2013, 2014), in parts of Asia (Chung, 2012; Dewan & Chen, 2005; Dineshwar & Steven, 2013; Donner & Tellez, 2008; Phonthanikitithaworn, Sellitto, & Fong, 2015; Sun et al., 2012; Zainudeen, 2011) , Kenya and parts of Africa (Etim, 2013; Evans & le Roux, 2016; Gakure et al., 2013; Gikunda et al., 2015; William Jack & Suri, 2011a; Kiiti, College, & York, 2011; Kusimba, Yang, & Chawla, 2015; Lule, 2012; Mbogo, 2010; Nyaga, 2013; Okiro & Ndungu, 2013; Plyler et al., 2010; Tobbin & Kuwornu, 2011), scarcity of literature still exists in Ghana. 4 University of Ghana http://ugspace.ug.edu.gh Bampoe (2015) also suggested that empirical investigations are not being explored at the centre of consistent business models. On four indicators of “mobile readiness” in Africa, Ghana, remarkably outranks Rwanda, Kenya, and Tanzania. According to a CGAP report “the most digital financial services-ready country in Africa” when it comes to the key elements essential for successful adoption is Ghana: 92% of adults have the required ID necessary to open an account and 91% of Ghanaians already own a mobile phone (compared to only 74% and 72% in Kenya and Tanzania, respectively) CGAP (2015). Per these readiness indicators, it is important to investigate mobile money adoption in the Ghanaian context. Lastly, most of the studies on mobile money adoption use theories of technology acceptance such as the Technology Acceptance model (TAM) (Dineshwar & Steven, 2013; Koenig-Lewis et al., 2010; Lule, 2012; Phonthanikitithaworn et al., 2015), Diffusion of Innovation theory (Al-Jabri & Sohail, 2012; Chung, 2012, 2013; Karsikko, 2015; Tobbin, 2012), a combination of TAM and DOI (Dineshwar & Steven, 2013; Gakure, Anene, Arimi, Mutulu, & Kiara, 2013; Islam, Khan, Ramayah, & Hossain, 2011; Karsikko, 2015; Kazi & Mannan, 2013; Koenig-Lewis et al., 2010; Lule, 2012; Tobbin & Kuwornu, 2011; Zhou, 2011), Theory of Planned behaviour (TPB) (Sun et al., 2012; Thakur & Srivastava, 2014) ,TAM and TPB (Chauhan, 2015; Khalifa & Shen, 2008), TPB and DOI (Alwahaishi & Snášel, 2013; Wei et al., 2009), Unified Theory of Acceptance and Use of Technology (UTAUT) (Bampoe, 2015; Kazi & Mannan, 2013; Lee & Song, 2013; Olasina et al., 2015). Slade & Williams (2013), argued that DOI was rather unsuccessful, explaining a low percentage of variance in behavioural intention, again, although TPB received quite a high variance in behavioural intention, the theory needed to be extended by crumbling precursors of attitudinal beliefs (Slade & Williams, 2013). Moreover, they believe that some studies using TAM 5 University of Ghana http://ugspace.ug.edu.gh on the other hand have been able to validate a high variance in behavioural intention and also TAM is the mostly used theory. Some studies employing the UTAUT model empirically validated the theory but excluded its moderators, although some IS theories have reached some level of maturity, same cannot be said for the UTAUT 2 model (Slade & Williams, 2013). Future research using age groups, diverse technologies and different countries were suggested by Venkatesh, Thong, & Xu, (2012) and also, Hughes & Lonie (2007) argued the need for future studies to examine the influence of price value on the adoption of the mobile money technology which is a construct of the UTAUT 2 model. A holistic view of the research in the discipline of mobile money adoption suggests that it is a rapidly emergent area for research in developing countries. Based on this, there is the need for more studies in the area since there are different avenues that can be explored by researchers. So far there are only a few studies in Ghana that have focused on mobile money adoption. Hence, by employing the UTAUT 2 model, the focus of this study is to respond to the call for more studies as emphasised by the above authors with the aim of contributing to the body of knowledge. 6 University of Ghana http://ugspace.ug.edu.gh 1.3 Research Purpose In relation to the limitations of previous research on the factors that influence the adoption of mobile money, the purpose of this study is to explore the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention, hedonic motivation, price value and habit on consumers' adoption of mobile money in Ghana. 1.4 Research Objectives With reference to the purpose of this study, the objectives are: 1. To explain the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention, hedonic motivation and price value on consumers' adoption of mobile money in Ghana. 2. To explore the determinants of mobile money adoption in Ghana. 1.5 Research Questions In order to achieve the research objectives and address the research problem the study considered two research questions: 1. What is the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention hedonic motivation, price value and habit on consumers' adoption of mobile money in Ghana? 2. What are the determinants of mobile money adoption in Ghana? 7 University of Ghana http://ugspace.ug.edu.gh 1.6 Synopsis of The Chapters This study focuses mainly on mobile money adoption in Ghana and the factors that influence its adoption. An overview of the chapters of the adoption of mobile money in Ghana have been outlined. Chapter 1: This chapter provides an introduction to the research. This was done by giving a brief background of the study; by pinpointing issues, theories, methods, and context gaps in the expanse of the research in order to establish the basis of the research; and proposing the research objectives and questions. Chapter 2: The primary focus of this chapter is examining extant literature on mobile money adoption in order to identify gaps that have been identified within the course of the research process enabling the unearthing of the existing body of knowledge and contributions of the phenomena.. Chapter 3: This chapter discusses the theoretical foundation of the study and seeks to provide a justification for the model adopted for the study based on literature. In this chapter justification of the choice of theory as the theoretical lens is also addressed and hypotheses are developed Chapter 4: This chapter discusses the general philosophical assumptions and methodologies and those employed in this study. This chapter further discusses the reasons for the choice of paradigm and methodology, data collection methods, instruments used as well as the method of analysis are also discussed. Chapter 5: This chapter presents an overview findings as evident from the data collection. It also discusses the demographic characteristics of the respondents used for the study and presents descriptive statistics of the scales used in measuring the constructs. The chapter also deals with 8 University of Ghana http://ugspace.ug.edu.gh validity checks through confirmatory factor analysis and presents an analysis of the relationship of the measured variables. Chapter 6: This chapter discusses the analysis of the findings relative to the data collection. The discussions are based on the relationship that exists between the endogenous and exogenous variables of the model; followed by discussions to check the effect of moderators such as gender, age and education on the exogenous variables in predicting the behaviour. Chapter 7: This chapter concludes by presenting a discussion of the summary, conclusion, limitations, directions for future studies, contributions or implications of the study to research, policy and practice, policy and research. 9 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO LITERATURE REVIEW 2.1 INTRODUCTION In relation to the previous chapter, the purpose of this study is to explore how factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, hedonic motivation, price value and habit influence the adoption of mobile money in Ghana. In this chapter, literature review focuses primarily on mobile money adoption in developing countries in order to contribute to knowledge on the subject matter and also to justify the need for this research (Webster & Watson, 2002). The review of related literature is categorized into four sections. The first section reviews general mobile money literature accompanied by the definition and conceptualization of mobile money. The second section seeks to ascertain research concerns by reviewing the underlying studies on mobile money. Methodologies and conceptual frameworks which have been adopted by existing researches on mobile money adoption are discussed in the third section. In the last section gaps in research which were unearthed discern during the course of the review of process are then discussed to establish the need for this study. 10 University of Ghana http://ugspace.ug.edu.gh 2.2 Mobile Money -An Overview In recent years, a dynamic tool that is considered by many to achieve or carry out professional and personal activities are cellular or mobile devices (Masamila et al., 2010). Mobile money is one of the many services that has emerged out of the numerous activities surrounding the mobile phone technology. The rise of mobile money services is forthcoming and so are its promising benefits to its users undoubtedly going to shape the telecommunications, technological and financial services industries. Advancing technological innovations and new transaction types are changing the mobile money landscape and opening up opportunities for a range of industry participants. The burgeoning growth in the last decade has made the mobile phone as imperative as the wallet. The ubiquitous nature of mobile communications has the potential to vastly improve and transform access to financial and transaction services for people, including the developing economies. Mobile money facilitates monetary transactions such as transfer of funds and other remittances such as utility bill payments and has successfully evolved in developing countries. Following Heyer and Mas (2010) “mobile money” includes elements such as: 1) An electronic stored-value account linked to user’s mobile phone 2) Mobile phone software (or “application”) that allows users to manage their accounts, and 3) A network of agents where user can exchange between cash and electronic value. 11 University of Ghana http://ugspace.ug.edu.gh 2.3 Definition of Mobile Money The definition of mobile money differs across the industry as it covers a wide scope of overlying applications in general, mobile money is a term describing the services that allow electronic money transactions over a mobile phone. It is also referred to as mobile financial services, mobile wallet, and mobile payment. In a current literature review, Diniz, de Albuquerque and Cerney, (2011) showed that “the majority of literature are based on developed world cases on mobile payments, with little or no reference to mobile money as a developmental tool”. Explicitly, it became crystallized as a payment system based on mobile phones after the first two "Mobile Money Summits" in 2008 and 2009 (Maurer, 2012).” It is also referred to as a suite of financial services offered through mobile phones and other handheld mobile devices (Dolan, 2009). Jenkins simply defined it as money that can be accessed and used via mobile phone (Jenkins, 2008). The key services included in the mobile money domain are person-to-person money transfer (domestic and international remittances); phone top-up (paying of credit units); mobile payment for retail transactions (including payment of bills) and mobile banking (Hughes & Lionie, 2007; Ivatury, Gautam & Mas, 2008). These services include the capability of turning a mobile device into a business tool, substituting or complementing banks, ATM and credit cards (Vashney & Vetter, 2002). The World Bank (2012) grouped mobile money into different types of financial services as mobile finance, mobile banking and mobile payments. Mobile finance includes credit, insurance and savings services. Mobile banking can be transactional or informational. Mobile payments range from payment made from person-to-person, government-to-person, and business-to- business. These types of financial services have traditionally belonged to commercial banks or microfinance institutions. 12 University of Ghana http://ugspace.ug.edu.gh Hughes and Lonie (2007) proposed that services such as bill payment, salary payment and local and international remittances could be included in mobile money. These included features and services are viewed by financial analyst as providing banking services to the unbanked. “Through the pay bill features available through mobile money services it is now possible to pay for electricity and water, digital television, parking fees and several other services. This is a rising trend among many consumers especially those in urban settings. The use of mobile money to pay bills is chiefly among wealthier, urban customers (Zutt, 2010). Must and Ludewig (2010) trace the rise of mobile money to the rapid and worldwide penetration of mobile phones back to 1999. However, mobile phone enabled commerce (m-commerce) or services may have started as early as 1997 when mobile phone enabled Coca Cola vending machines and mobile phone banking services were introduced in Finland. Earlier documented mobile commercial services include a Philippine mobile operator’s launch of SMART money in 1999. By the year 2000, mobile money technology had started to spread to include several other countries. Later GLOBE Telecom launched G-cash in 2004 (Wishart, 2006). Bharti Airtel launched their mobile money transfer pilot project in India in 2007 (Bosi, Celly & Joshi, 2011). 2.4 The Adoption of Mobile Money The acceptance, use and adoption of mobile money as a phenomenon of interest are contemporary and scarcely researched. Studies from the development/practitioner literature dominate research in this area (Ivatury & Pickens, 2006; McKay & Pickens, 2010; Porteous, 2006). However, since the launch of SMART Money in the Philippines in 2003, there are currently 145 mobile money deployments which have been launched across 73 developing countries (MMU, 2012). There is a further 104 deployment planned. The year 2010 alone saw 31 new mobile money deployments in 13 University of Ghana http://ugspace.ug.edu.gh 25 countries. There are varied business models including Mobile Network Operator-led, Bank-led and mobile content provider-led. Currently, the most successful deployment of mobile money is M-PESA in Kenya. Since its launch in March 2007, it has been adopted by 11.7m customers (corresponding to 54% of Kenya’s adult population and 73% of Safaricom’s subscriber base) and routes extra transactions nationally than Western Union does globally. Following a study of the elements affecting the adoption of mobile banking by the poor in South Africa, Ismail &Masinge (2011) found perceived usefulness, perceived ease of use and affordability as the key determinants of consumer adoption of mobile banking. However, perceived risk was found not to influence the adoption of mobile banking significantly. In a separate empirical study by Ajo et al., (2012), 67% of the sample population gave a rating of five (excellent) and four (good) that they felt comfortable using the M-PESA services. Further 87% of the sample reported that the system was easy to learn how to use. 2.5 Mobile Money Ecosystem The terminology business ecosystem was developed from biological ecosystem perspective coupled with the study of business networks. Initially used by Moore (1993), he defined a business ecosystem as an economic community (made up of the suppliers, customers, partners, competitors and other stakeholders) supported by a foundation of interacting organizations and individuals – the organiShort Message Service of the business world (Moore, 1996). Through the lens of the business ecosystem concept, one puts focus on the interconnectedness of the various actors and the fact that they depend on each other for survival (Peltoniemi, 2005; Iansiti and Levien (2004b). Like Moore (1993), Iansiti and Levien (2004b) argued that no firm can work in isolation and that the health and performance of a firm is dependent on the health and performance of the whole 14 University of Ghana http://ugspace.ug.edu.gh business community. They went further to develop metrics for the measurement of the health of ecosystems and they proposed robustness, productivity and niche creation as key elements. Furthermore, they developed innovation and operation strategies that a firm can adopt depending on its role in the ecosystem (Hartigh & Asseldonk, 2004). Extending the business ecosystem to the mobile money environment, Jenkins (2008) and Tobbin (2011) suggest that there are a number of key players in the mobile money ecosystem – including consumers, Mobile Network Operators (Mobile Network Operators), banks, agents, merchants, competitors and regulators. They share a common fate in the ecosystem. There are however other stakeholders: micro-financial institutions, international financial institutes and donors, and civil society, who could contribute to the ecosystem. However, none of them play a vital role (Jenkins, 2008). Below are the key stakeholders and their roles in the ecosystem using Tobin’s (2011) model. Figure 2. 1 The Mobile Money Ecosystem Source: Tobin (2011) 15 University of Ghana http://ugspace.ug.edu.gh Table 2. 1 Players in the mobile money ecosystem Players Roles Limitations and Constraints Mobile network operators 1.Provide infrastructure and 1. Regulatory limitations on communications service providing financial services 2. Provide agent oversight and 2. Shareholder pressure for faster, quality control higher returns 3. Issue e-money (where permitted 3. Strategic focus that may not by law) include mobile money 4. Exercise leadership in drawing mobile money ecosystem together 5. Advise other businesses (banks, utilities, etc.) on their mobile money strategies Financial Institutions 1.Offer banking services via mobile 1.Narrow customer base 2. Hold Float or accounts in 2.Lack of experience with or customers’ names interest in low-income customers 3.Handle cross-border transactions, 3.Stringent regulatory requirements manage foreign exchange risk with significant compliance burdens 4.Ensure compliance with financial sector regulation Agents 1.Perform cash-in and cash-out 1.Liquidity shortfalls functions 2.Basic business skill gaps 2.Handle account opening 3.Lack of customer trust (in some procedures, including customer due cases) diligence Report suspicious 4.Limited ability to partner with transactions in accordance with large corporations AML/CFT 3.equirements 4.Identify potential new mobile money applications Regulators 1.Provide enabling environment for 1.Lack of experience with 2. mobile money Convergence of financial and 2.Protect stability of financial telecommunications regulatory system schemes 3.Demonstrate leadership to 3.Lack of financial and technical encourage and protect behaviour capacity change Consumers 1.Use mobile money to improve 1.Lack of awareness their lives 2.Limited financial literacy 3.Cultural and psychological resistance Source: Jenkins (2008) 16 University of Ghana http://ugspace.ug.edu.gh 2.6 Mobile Money Transfer Process The mobile money includes a number of interconnected systems that form the mobile money network. It is a client server based system with the client application residing on the SIM card, a chip that identifies the subscriber's phone number, connecting to Mobile Network Operator’s m- commerce server. When started, the application connects to the Mobile Network Operator's network and uses the Short Message Service protocol to communicate with the m-commerce server. A mobile money transfer usually will involve 4 steps: registration, cash-in, transfer, and cash-out. A onetime registration process (i) is required before a user can use any of the mobile money services. The registration process is usually free. A customer visits an agent and fills in an application form. The agent verifies the customer’s ID (a national ID, passport, or driving license etc.) then uses his or her phone to register the customer temporarily on the Mobile Network Operator’s m -commerce server. An account with a mobile wallet (m-wallet) is created on the m- commerce server and Short Message Service (SMS) confirmation is sent to the customer. The customer selects a secret PIN, which becomes his or her main validation pin for all future transactions. The cash-in process (ii) involves the purchasing of electronic money (e-value) into the m-wallet. The customer then visits an agent and pays an amount of electronic value. The agent transfers the e value from his/her special SIM mobile phone to the customer through the m- commerce server. Further, an encrypted Short Message Service is sent from the agent’s mobile phone to the m-commerce server, requesting for, the transfer to be drawn between the two accounts. A Short Message Service is sent to the customer to confirm the transaction. The next step is the actual transfer stage (iii). This is usually done through the customer inter face on a basic model phone. To select the best method, which provides a compromise between usability, security and costs, most implementations use a menu driven access by the SIM toolkit, which is the standard 17 University of Ghana http://ugspace.ug.edu.gh software on all mobile phones (Hughes & Lonie, 2007). The customer using the menu on the SIM transfers the e-value from his/her phone to the recipient’s mobile wallet. This involves an encrypted Short Message Service to the m-commerce server from the sender with an instruction to transfer the specified amount to the recipient. After verification and availability of funds checks, the m-commerce server instructs by debiting the sender’s account with the amount and any fees charged (where applicable) and crediting the recipient. A confirmation through a Short Message Service is sent to both the sender and the recipient. 2.7 Theoretical Framework This chapter features a review of related literature on mobile money adoption in developing countries in order to facilitate the advancement of knowledge on the phenomenon, unearth new research areas and justify the need for this study (Webster & Watson, 2002). Due to head-way in technology, many models and theories have been presented in attempt to explain the factors that cause people to accept and use new technologies. Commonly among these theories are their extensions to value added mobile services (Barnes and Huff, 2003; Biljon et al, 2008; Carlssson et al. 2006; Chen, 2008; Muk, 2007; Teo & Pok, 2003) The main models advanced in information and communication studies literature include the Technology Acceptance Model (TAM), the Innovation Diffusion Theory (IDT) (Rogers, 2003), and the Unified Theory of Acceptance and Use of Technology (UTAUT) and Unified Theory of Acceptance and Use of Technology 2(UTAUT2) (Venkatesh et al., 2003). 18 University of Ghana http://ugspace.ug.edu.gh 2.7.1 Technological Acceptance Model (TAM) TAM is an adaptation of Fishbein and Ajzen‘s Theory of Reasoned Action (TRA) that proposes that behaviour is a direct consequence of behavioural intention, (Koenig-Lewis et al., 2010). Most literature on mobile services show that TAM is the most widely used, validated and replicated theoretical model in the prediction of future consumer behaviour (Luarn& Lin, 2005; Dass & Pal, 2011; Adams et al 1992; Chau & Hu, 2001; Davis & Venkatesh, 1996; Kwon & Chidambaram, 2000; Legris et al, 2003). Davies argues that the intention to use a particular technology is based on a person’s behavioural intention which is determined by two beliefs; perceived ease of use and perceived usefulness (Liu & Li, 2009; Sangle & Awasthi, 2011). Many authors have established that the TAM constructs are insufficient in examining a user’s acceptance of mobile money services and have employed different extended versions of the model (Nysveen et al., 2005, 330- 31; Lule et al., 2012, 33; Luarn & Lin, 2005; Venkatesh & Davis, 2000). A study by Bagozzi (2007) found that TAM does not take into account the role of group, cultural and social aspects of decision making which are central to technology adoption, and advocates for the model extension. Other studies suggest perceived risk; perceived credibility, financial cost, self-efficacy, and relative advantage as other factors that affect mobile financial services adoption not covered by the TAM model (Donner & Tellez, 2008; Riquelme & Rios, 2010; Luarn& Lin, 2004; Wang, 2003). 2.7.2 Diffusion of Innovation (IDT) The other widely used theory is IDT, which various literature argues complement the TAM (Koenig-Lewis et al., 2010; Venkatesh et al., 2003). Tobbin (2011) presented a framework that 19 University of Ghana http://ugspace.ug.edu.gh utilizes the constructs of perceived trust, transactional cost and perceived risk in addition to the key constructs of the TAM and the IDT theory to explain the acceptance and use of mobile money transfer services among Ghanaian consumers. IDT helps to understand customer’s behaviour in the adoption or non-adoption of an innovation (Vaugh and Schavione, 2010; Lee et al., 2003). In the theory, diffusion is defined as ―the process by which an innovation is communicated through certain channels over time among the members of a social system‖ (Rogers, 2003).The theory highlights five perceived characteristics that influence the adoption and non-adoption of an innovation which are: relative advantage, perceived compatibility, simplicity or complexity of use, trialability and observability (Rogers, 2003; Koenig-Lewis et al., 2010; Lee et al., 2003) as the key characteristics that enable an innovation to be taken up by a population. The main construct of the theory includes, relative advantage, complexity, compatibility, observability, trialability. 2.7.3 Unified Theory of Acceptance and Use of Technology (UTAUT) A broad, powerful and robust theory that consolidates TAM, IDT and other models is the Unified Theory of Acceptance and Use of Technology (UTAUT) model, developed by Venkatesh et al., (2003). Zhou (2011) asserts that it is robust than other theories of technological adoption. The UTAUT aims to explain user intentions to use an Information Systems (IS) and subsequent usage behaviour. The theory holds that four key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) are direct determinants of usage intention and behaviour (Venkatesh et. al., 2003). Koenig-Lewis et al., (2010) in their study came out with five dimensions. The dimensions include perceived usefulness, compatibility, cost of use, ease of use, and perceived trust. Sun et al., (2012) also identified five dimensions, perceived usefulness, perceived credibility, perceived financial cost, perceived expressiveness and subjective norm. 20 University of Ghana http://ugspace.ug.edu.gh 2.8 Conceptual Framework (UTAUT2) The Unified theory of adoption and use of technology 2 (UTAUT 2) by Venkatesh et al considered to add different concepts to the model so as to widen the theoretic relationships of UTAUT. The UTAUT 2 has gradually been adopted for considering issues such as self-technology service, mobile phones adoption and other related issues. The UTAUT 2 model is an extension from the UTAUT and focuses more on individual perspectives in technology adoptions. This new model was enhanced for explaining differences in users’ intention for using technology. Since the main objective of this research is explore factors influencing individual users, adoptions of Mobile Money, the UTAUT2 framework can provide more details and thus, will be adopted for this paper as the research model. The eight models and theories of individual acceptance that are compounded by Venkatesh et al. (2003) include the Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of Planned Behaviour (TPB), As a result of the above, this study has therefore identified the following UTAUT 2 variables which consolidates other variables(perceived risk, perceived trust, perceived ease of use, perceived cost, and perceive usefulness, social influence, competitive intensity), relevant to technology adoption in previous studies. 21 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE RESEARCH FRAMEWORK 3.1 INTRODUCTION This chapter discusses the theoretical foundation adopted in the study. The previous chapter brought to light the existing theoretical gap, which is the limited use of the Unified Theory of Acceptance and Use of Technology (UTAUT 2) theory in IS literature. This study aims at contributing to academic research by using the UTAUT 2 theory. This theoretical foundation was adopted because its constructs - performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation and price value will be most suitable in the answering of the research questions and the unearthing of various issues surrounding this research area in order for researchers to obtain a unified view of technology adoption by consumers. This section discusses literature relating to the chosen theoretical framework in order to build research hypothesis on the influence of performance expectancy, effort expectancy, social influence facilitating conditions, hedonic motivation and price value of consumers' adoption of mobile money in Ghana. 3.1 Overview of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) Venkatesh et al. [2003] empirically compared eight competing models in their quest to understand technology adoption. Based on their longitudinal studies, Venkatesh et al. [2003] further integrated and refined the eight models into a new model named UTAUT which captures the essential elements of different models. The UTAUT not only emphasize the core determinants predicting the intention to adopt and actual adoption, but also allow researchers to analyze the contingencies from moderators that would increase or limit the effects of core determinants. 22 University of Ghana http://ugspace.ug.edu.gh Moderating variables that had been reported in literature as having an influence on information systems adoption and usage decisions were also considered. However, it was realized that, with the exception of motivation model and social cognitive theory, there was an increase in the predictive validity of the models after the inclusion of the moderators. Venkatesh et al. (2003), also investigated the harmony among these models and hypothesized that five constructs play a significant role as direct determinants of user acceptance and usage behaviour. These constructs were performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention. Based on a further review of the extant literature, Venkatesh et al. (2012) proposed the extension of UTAUT, to what they termed UTAUT2 by identifying key additional constructs and relationships to be integrated into UTAUT, thus tailoring it to a consumer use context. They accomplished this by (1) identifying three key constructs from prior research on both general adoption and use of technologies, and consumer adoption and use of technologies, (2) altering some of the existing relationships in the original conceptualization of UTAUT, and (3) introducing new relationships. Venkatesh et al. (2012) suggested that both consumer behavior and IS research have theorized and found various constructs related to hedonic motivation as important in consumer product and/or technology use and integrating hedonic motivation will complement UTAUT’s strongest predictor that emphasizes utility. Secondly, in consumer contexts, unlike workplace contexts, users are responsible for the costs and such costs, besides being important, can dominate consumer adoption decisions. Adding a construct related to price/ cost will complement UTAUT’s existing resource considerations that focus only on time and effort. Finally, recent work has challenged the role of behavioral intention as the key predictor of technology use 23 University of Ghana http://ugspace.ug.edu.gh and introduced a new theoretical construct (i.e., habit) as another critical predictor of technology use. Venkatesh et al. (2012) defined the seven constructs as: Performance Expectancy is defined as the degree to which using a technology will provide benefits to consumers in performing certain activities. Effort Expectancy refers to is the degree of ease associated with consumers’ use of technology. Social Influence refers to the extent to which consumers perceive that important others (e.g., family and friends) believe they should use a particular technology. Facilitating Conditions refer to consumers’ perceptions of the resources and support available to perform a behaviour. Hedonic motivation is defined as the fun or pleasure derived from using a technology. Price value is defined as consumers’ cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them. Habit is defined as the extent to which people tend to perform behaviors automatically because of learning. Venkatesh et al. (2003) noted that researchers typically pick and choose constructs across models/theories or take all constructs from a favourite model. This research therefore chooses six out of the seven constructs which are performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation and price value to measure how they each affect behavioural intention (see Figure 3. 1). 24 University of Ghana http://ugspace.ug.edu.gh Figure 3. 1 Research Model H1 Performance Expectancy Effort H2 Expectancy H3 Behavioural H7 Social Influence Use of Behavior Intention Hedonic H4 Motivation Facilitating H5 Conditions H6 Price Value Source: (Venkatesh et al., 2012) 25 University of Ghana http://ugspace.ug.edu.gh 3.2 Hypothesis Development 3.2.1 Performance Expectancy (PE) This construct refers to the degree to which using a technology will provide benefits to consumers in performing certain activities. This construct is made up of constructs of other models that are considered as having a relation with performance expectancy. These constructs include: perceived usefulness (TAM, and combined TAM-TPB); extrinsic motivation (MM); job-fit (MPCU); relative advantage (DOI); and outcome expectancy (SCT). Employing UTAUT and UTAUT 2, Thakur & Srivastava (2013), Slade et al. (2014), Venkatesh et al. (2011) also concluded that performance expectance significantly influenced people to adopt mobile technologies but a similar conclusion was not achieved by Bampoe (2015). H1: Performance expectancy (PE) will positively influence consumers’ intention to adopt mobile money in Ghana. 3.2.2 Effort Expectancy (EE) Effort expectancy on the other hand represents the degree of ease associated with consumers’ use of technology. Other constructs in different models also capture this same concept. They include: perceive ease of use (TAM); and complexity (DOI and MPCU). However, the relationship between effort expectancy and behavioural intentions is often debated due to the effect of performance expectancy on behavioural intention. Grounded in UTAUT, Thakur & Srivastava (2013), employed four constructs of performance expectancy, effort expectancy, facilitating conditions and social influence to explore what influences individual intention to accept mobile technology. Mortimer, Neale, Hasan, & Dunphy (2015) and Bampoe (2015) supported that effort expectance significantly influenced human intention to use mobile technology or service. In view of this, we 26 University of Ghana http://ugspace.ug.edu.gh conclude that a positive relationship exists between perceived ease of use and intention to adopt a system. Hence, the hypothesis: H2: Effort expectancy (EE) will positively influence consumers’ intention to adopt mobile money in Ghana. 3.2.3 Social Influence (SI) Social influence refers to the extent to which consumers perceive that important others (e.g., family and friends) believe they should use a particular technology. This particular construct is represented differently in existing models such as subjective norms (TRA, TAM2, TPB/DTPB and combined TAM-TPB), social factors (MPCU), and image (DOI). Al-Qeisi (2009) has posited that a comparison between models established that the behaviour of these construct in relation to the adoption of new systems have some similarities. Slade et al. (2014), Zainudeen (2011), Bampoe (2015) empirically found that individual intention to use mobile money was significantly affected or influenced by friends and family members. Based on this review of literature, social influence can be considered as having a positive influence on behavioural intention of consumers to adopt mobile money in Ghana. Hence, the hypothesis: H3: Social influence (SI) will positively influence consumers’ intention to adopt mobile money in Ghana. 3.2.4 Hedonic Motivation (HM) Hedonic motivation is the pleasure acquired from the use of technology (Brown & Venkatesh, 2005). This construct will affect behavioural intention due to differences in consumers’ 27 University of Ghana http://ugspace.ug.edu.gh innovativeness, novelty seeking, and perceptions of novelty of a target technology. Innovativeness is “the degree to which an individual is receptive to new ideas and makes innovation decisions independently” (Midgley and Dowling 1978). Novelty seeking is the tendency of an individual to seek out novel information or stimuli (Hirschman 1980). Such innovativeness and novelty seeking can add to the hedonic motivation to use any product (Holbrook and Hirschman 1982). HM plays a vital role in predicting the motives for use of technology (Venkatesh et al, 2012). Venkatesh et al. (2011) Zarmpou, Saprikis, Markos, & Vlachopoulou (2012) and Huang & Kao (2015) are a few studies that support hedonic motivation or innovativeness as a determinant for technology adoption. Hence the hypothesis: H4: Hedonic Motivation (HM) will positively influence users’ behavioural intention to adopt mobile money in Ghana. 3.2.5 Facilitating Conditions (FC) Facilitating conditions refer to consumers’ perceptions of the resources and support available to perform a behaviour. This is because many aspects of facilitating conditions, such as training and support provided, will be freely available within an organization and fairly invariant across users. In contrast, the facilitation in the environment that is available to each consumer can vary significantly across application vendors (Venkatesh et al., 2012). In this context, facilitating conditions will act more like perceived behavioral control in the theory of planned behavior (TPB) and influence both intention and behavior (Ajzen 1991). Studies such as Venkatesh et al. (2011), Mukherjee (2015), Thakur (2013), Slade & Williams (2013), and Bampoe (2015) conclude that facilitating conditions are indeed a contributing factor to mobile money adoption. Thus, the hypothesis: 28 University of Ghana http://ugspace.ug.edu.gh H5: Facilitating conditions (FC) will positively influence consumers’ adoption of mobile money in Ghana. 3.2.6 Price Value (PV) Price Value is defined as consumers’ cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them. The price value is positive when the benefits of using a technology are perceived to be greater than the monetary cost and such price value has a positive impact on intention. Thus, price value is a predictor of behavioral intention to use a technology as highlighted by Venkatesh et al. (2011) and Slade & Williams (2013). H6: Price Value will positively influence users’ behavioural intention to adopt mobile money in Ghana 3.2.7 Behavioural Intention (BI) Supporting the various models from psychological theories, which argue that individual behaviour is predicted and influenced by individual intention, the UTAUT model contended and proved behavioural intention to have significant influence on technology usage (Venkatesh et al. 2003; Venkatesh & Zhang 2010). Therefore, for the purpose of maintaining consistency with the underlying theory for all the intention models, behavioural intention is expected to have a significant positive influence on the usage of a new system (Venkatesh et al., 2003). Considering that the ultimate goal of every business is to attract consumers to adopt a system rather than their intention to adopt, it is necessary for writers to examine the relationship between behavioural intention and actual usage. Thus, it can be hypothesized that: 29 University of Ghana http://ugspace.ug.edu.gh H7: Behavioural intention (BI) will have a significant positive influence on mobile banking usage behaviour in Ghana. 3.3 Summary In summary, the chapter comprised of the theoretical foundation used for the study, that is, The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The chapter discussed the UTAUT 2 model and provided an overview, detailed description and explanation of the theory as posited by Venkatesh and other relevant researchers that are directly related to the studied area. The chapter also showed empirical evidence of the extent to which the UTAUT 2 framework has been used in prominent studies. The chapter then proposed specific hypotheses under each construct of the framework with the view of satisfying the first and second research questions set out at the beginning of the study. 30 University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR RESEARCH METHODOLOGY 4.1 INTRODUCTION The chapter starts by discussing some philosophical and methodological issues that are related to this study. The methods of quantitative and qualitative research are also discussed in this chapter with justification being made in line with the research approach and philosophy underpinning the study for choosing quantitative method. The data collection method as well as the instruments used in data collection, analysis and quality criteria are discussed. Moreover, research hypotheses are drawn and operational definitions and the research instrument are presented. Finally, aspects of research design and methods are discussed, followed by issues pertaining to compatibility and equivalence, research ethics and statistical tools used. 4.2 Research Paradigm A paradigm is defined as a “set of beliefs, values and techniques which is shared by members of a scientific community, and which acts as a guide or map, dictating the kinds of problems scientists should address and the types of explanations that are acceptable to them” (Kuhn, 1970). A research paradigm is constituted by three dimensions namely, Ontology, Epistemology, and Methodology (Lincoln et al., 2011). The ontological dimension of a research paradigm looks at the nature of a phenomenon and determines if it is objective and distinct from the researcher or is created by the action of the researcher. On the other hand, Epistemology is concerned about the nature of knowledge (Ritchie & Lewis, 2003) and whether it is made and assessed by verifying empirically a theory or whether the knowledge is created by the interaction of the researcher with the social context (Rowland, 2003). Lastly, the methodological dimension of a research paradigm is 31 University of Ghana http://ugspace.ug.edu.gh concerned with the methods involved in data collecting and analysis for drawing a valid conclusion during a research project for example, quantitative, qualitative or mixed (Lincoln et al., 2011). The two dominant paradigms that have evolved over the years in information systems research are Positivist and Interpretive/Constructivist (Mingers, 2004). The two major paradigms have their respective view of issues and dictate how social phenomena can be studied. The positivist research paradigm holds that objective reality can be observed empirically and explained with logical analysis. This paradigm maintains that the researcher and the study must be seen as separate entities. Thus, it is assumed that the positivist researcher is neither affected by the subject under study nor does he affect the subject under study since the researcher is deemed as being independent of the subject of research (Rermenyi, Williams, Money, & Swartz, 1998). Hence, it uses direct observation in establishing facts (Krauss, 2005). Positivism adopted Rene Descartes’s epistemology and belief that the best way to generate knowledge about reality is through reasoning (Descartes & Cress, 1998). The assumption of positivism is not only based on the existence of reality or the real world that exists beyond the cognition of human beings, but also on the acquisition of objective knowledge of reality or the real world (Weber, 2004). However, ontological positivist argues that the existence of reality is not only beyond human creation but also on action and knowledge of human beings (Orlikowski & Baroudi, 1991). Information systems researchers who adopt the positivist paradigm focus their emphasis on measurable quantifiable variables, hypothesis testing based on phenomena samples from a population acknowledged in the study and the preposition of formal evidence. Smith (1991) and 32 University of Ghana http://ugspace.ug.edu.gh Guba and Lincoln (1994) have argued that positivist adopt scientific methods from the natural science in studying social phenomenon. Therefore, epistemological positivists have indicated that knowledge can be attained by the independent and objective study of reality, even though there exists objective knowledge. Interpretive study seeks to understand the contextual meaning people assign to social phenomenon (Myers, 1997; Orlikowski & Baroudi, 1991; Walsham, 2006). The assertion of interpretivist is that social construction like language consciousness and shared meaning is what reality is accessed through (Myers, 1997). Therefore, they focus fully on the human sense and its complexity as events emerge (Kaplan & Maxwell, 2005). Walsham (1995a) has therefore said that reality under interpretivism can be group into two forms: inter-subjective reality constructed between researchers and their respondents and subjective reality which is constructed by an individual or groups of people. In contrast with positivist, the interpretivist believe that knowledge is humanly composed, therefore fact and values are subjective (Walsham, 1995a, 2006). In essence, knowledge without the researcher is unrealistic since the experiences drawn from the researcher may help in steering the study. Furthermore the perception of the respondent and the researcher may change as a result of the interaction with each other during the study (Myers, 1997). 4.2.1 Positivist Paradigm Chosen: Why Positivism? In each of the natural, social and human sciences the scholarship intended to generate new knowledge is informed by the research paradigm (McGregor & Murnane, 2010). Therefore, after explaining the various paradigms that have dominated information systems research over a period 33 University of Ghana http://ugspace.ug.edu.gh of time, this particular section of the chapter is to emphatically state the reason for choosing one of these paradigms. Hirschheim (1985) has said that the renaissance of the positivist paradigm came in the sixteenth and seventeenth centuries after a long, dark period in European scientific thought. Until the late 1970s information systems research and organization science was dominated by interpretivist (Vreede, 1995), however, the positivist paradigm has taken over (Dickson & DeSanctis, 1990). According to Weber (2004), positivists do not only assume the existence of reality or the real world that exists beyond the cognition of human beings, they also assume that acquiring the objective knowledge of reality or the real world is possible. Corbin and Strauss (2008) have said that the intention to identify regularities in, and to form association between some elements, through the manipulation of reality with variations in only a single independent variable is called positivism. Information systems researchers have, over the years, used the positivist paradigm in various works, causing Orlikowski and Baroudi (1991) to posit that there is a clear dominance of the positivist perspective in the field of information systems literature. Similar to this accession, Guba (1990) and Chen and Hirschheim (2004) have also opined that 81% of published empirical information system research is dominated by positivist research. Unlike the positivist paradigm, the interpretivist paradigm was not chosen because interpretive authors (i.e. (Guba, 1990) hinder the development and use of systematic standards for research quality judgment by stating equivocally that they adopt an unqualified or strong relativism, which is logically self-refuting. Although researchers ought to respect the opinions and views of different people and different groups, in dealing with human research there is the need for researchers to understand and depict individual or social group differences in order to adopt a democratic 34 University of Ghana http://ugspace.ug.edu.gh approach to group opinions for value selection. On this basis, using the interpretive paradigm for this research was not appropriate. 4.3 Research Approach Benbasat, Goldstein, and Mead (1987) have stated that from observation it is very clear that no single research methodology is intrinsically better than the other. Other authors such as Benbasat (1984) and Pervan (1994) have stated that the best methodology suitable for any study should be dependent on the research problem under consideration, the richness and complexity of the real world and the stated objective of the researcher. In essence, the choice of the kind of methodology a researcher adopts is usually based on the goal of the researcher and not the paradigm (Cavaye, 1996). Despite the existence of many research method classifications, the most dominant group of classification are the quantitative and the qualitative (Johnson & Onwuegbuzie, 2004; Myers, 1997). Detailed descriptions of the two most dominant research methods are stated below. According to Hall & Howard (2008), researchers need be motivated to acknowledge paradigmatic differences while attentively selecting the methods that provide the greatest opportunity for cross- paradigm communication within the study design. Other authors advise that researchers adopt a perspective compatible with their research interest and at the same time remain open to the use of other interests (Orlikowski & Baroudi, 1991). Webber (2004) suspects that the different choice of research methods is largely due to factors such as type of training provided for the researcher, social pressures associated with advisors and colleagues and preferences of obtaining certain types of insight during the research. 35 University of Ghana http://ugspace.ug.edu.gh Based on the arguments presented in the previous sections and from the extant literature critically reviewed and presented in chapters two and three, a number of points can be shown as leading to the choice of an approach for the current study. First, from the vast body of research on technology acceptance it seems that technology acceptance research has a dominant theoretical drive which is positivist in nature. Second, according to Maxwell & Loomis (2003), interactive design and content analysis define the research approach: the current research is defined as predicting the viability of a model in the context of a developing country Ghana contexts and the technology acceptance behaviour, this study requires the means of a more structured, well-defined framework, precise measurement (prior development of instrument standardization), comparison of variables, establishing relationships between variables and making inferences from samples to populations, all of which can be seen under the quantitative research umbrella. Also, the major thrust of the current research project is to test hypotheses related to the proposed model, as well as a number of hypothesized relationships that were previously established in the technology acceptance context; hence, the theoretical thrust of the current research is deductive in nature and the use of inductive reasoning is excluded from the interpretation of results when compared to previous findings from literature. Furthermore, the current study follows a confirmatory strategy of research, one that envisions empirical analysis as a process of confirming or disconfirming previously stipulated hypotheses in the technology acceptance context. Fifth, the current study aims to conduct a number of group comparisons; this test is effectively using Structure Equation Modelling technique, which is 36 University of Ghana http://ugspace.ug.edu.gh utilized only through statistical packages. Moreover, the researcher is versed in statistics, which makes it a personal preference to work with a quantitative approach 4.3.1 Why the Quantitative Method of Research? Ardent dispute has evolved around advocates of qualitative and quantitative research paradigms. With quantitative researchers consistently articulated assumptions that are linked with the philosophical thinking of positivism (Ayer, 1959; Maxwell & Delaney, 2004; Popper, 1959; Schrag, 1992). Quantitative purist has indicated that, like the treatment of physical phenomenon by physical scientists, social observation should be treated as entities. Quantitative researchers have also stated that social science inquiry should be objective and hence, the researcher must be separated from the research. However, qualitative pursuits on the other hand have strongly opposed the philosophical ideals of positivism. Guba and Lincoln (1989) have said that the qualitative researchers usually argue for constructivism, idealism, relativism, humanism, hermeneutics and sometimes postmodernism. Thus, they contend that time and context free generalization are neither desirable nor possible, since multiple constructed realities abound and research is value bound. Based on this, they assume that the flow of logic from specific to general is not right, and that, it is impossible to differentiate fully between causes and effects. They also believe that the knower and known cannot be separated because the only source of reality is the subjective knower (Guba, 1990). It therefore becomes very clear that both methods are extremely relevant in understanding a phenomenon. In essence, it becomes impossible to identify one as being better than the other. In view of this, Newman and Benz (1998) have posited that an approach that serves to answer a specific research question or problem is deemed as the appropriate approach for a particular study. Therefore, the study chose the quantitative approach as the appropriate approach for the study 37 University of Ghana http://ugspace.ug.edu.gh taking into consideration the research problem and the research questions outlined in the first chapter. This method of research allows for statistical analysis and generalization of a large context beyond the studied objects (Bryman, 2001). This method of research is linked with the positivist paradigm which also allows for context free generalization (Nagel, 1986). Accordingly, Neuman and Neuman (2006) argue that writers that employ the use of the quantitative approach to research are likely to apply reconstructed logic. This implies that the logic of how to do research is highly organized and restated in an idealized, formal, and systematic form. Based on this, the quantitative research method is usually used to measure how people feel, think or act in a particular way. It seeks to quantify data and apply some statistical analysis which are often formalized and well- structured. Data used for quantitative studies are usually obtained from large samples (i.e., anything from 50 upwards) (Tull & Hawkins, 1990). As a result, it usually employs the use of a structured questionnaire that incorporates closed-ended questions that are made up of already set responses. Based on this, the quantitative approach is deemed as being concise and having a sample that is representative of a large population (Yin, 1994). 4.4 Research Strategy According to Saunders et al. (2011), there are three basic forms of research purpose that includes explanatory, descriptive and exploratory research. Descriptive method of research refers to the type of research that is aimed at obtaining information about the current state of a phenomenon. The use of a descriptive form of research enables the researcher to describe the existing condition 38 University of Ghana http://ugspace.ug.edu.gh properly. As the name suggests, the main focus of descriptive research is to accurately provide the description of an observed phenomenon. Polit, Beck, and Hungler (2001) has posited that descriptive research seeks to naturally describe, observe and document an occurring phenomenon which cannot be ascribed an objective value. On the other hand, for the purpose of explaining the relationship that exists between variables, Sullivan (2001) has posited that an explanatory research purpose helps this type of study. For instances, an explanatory form of research seeks to find the reason behind the facts established by a quantitative study. It also seeks to identify the actual reason behind the occurrence of a particular phenomenon. Therefore, explanatory research is usually in the form of a qualitative study. However, exploratory research can be defined as a research type that helps one gain fresh insight into a situation or a phenomenon in order to build, elaborate, extend or test a theory (Neuman & Neuman, 2006). This form of research purpose is usually adopted in the early stages of research or in research where the concepts are not clear enough to enable the researcher to develop an operational definition and properly design the research (Saunders et al., 2011). Therefore, the objective of exploratory research is to identify key issues and key variables. Therefore, in studying an issue like the factors that influence mobile adoption in Ghana, where the variables are not clear enough for the development of an operational definition, using an exploratory research method remains the most appropriate research purpose for the study. Whilst data can be collected about the real world environment, the use of a survey enhances the studying of numerous variables at an instance than what laboratory or field experiments typically permits. Based on this, the study used the survey approach which is also linked with the positivist paradigm (Neuman, 2011). Hair, Tatham, Anderson, and Black (2006), have said that the use of a 39 University of Ghana http://ugspace.ug.edu.gh survey in studying the cause of a phenomenon with empirical evidence in relation to attitude and behaviors of organizations is deemed to be most appropriate. This is because, in studying a sample of a population, the survey provides a numeric or quantitative description of trends, attitudes or opinions of the population (Creswell & Clark, 2007). In light of this, Nesbary (1999) has defined survey research as “the process of collecting representative sample data from a larger population and using the sample to infer attributes of the population” (p. 10). Quantitative studies are mostly classified into two: studies that describe events; and studies that discover inferences or causal relationships. Descriptive research is aimed at finding what already exists and thus observational survey methods are frequently used to collect data (Borg and Gall, 1989). On this basis, the use of the observational method of data collection is inappropriate; hence this study will focus on discovering inference or causal relationships. Due to the strict privacy rules of banking, the observational method of collecting data is nearly impossible; more so when the quantitative approach to research is being used. In essence, the survey method was used in collecting data for this study. A research strategy is an all-purpose method the researcher anticipates using, in order to answer the research questions effectively. Thornhill et al. (2003) opined that a research strategy includes clear objectives which are obtained from the research questions, to specify the basis that will be used to gather data and also put in consideration the limitations associated with the research, and includes time, money, data access, location and other ethical issues. 40 University of Ghana http://ugspace.ug.edu.gh 4.4.1 Survey The survey is usually related with the deductive approach, and is a frequently used method in a research (Seyed, 2008). It entails gathering a large quantity of data, which includes questionnaires, semi-structured interviews and structured observations in a highly efficient manner, appropriate for the study (Thornhill et al., 2003). A survey can also be used for collecting primary and secondary data from a sample size according to Collis and Hussey (2009), by assessing them in figures and simplifying the results to a population. 4.5 Sampling Techniques Survey sampling describes the process of selecting a sample (a smaller number of subjects) of elements from a target population in order to measure the characteristics and/or attitudes of people. The survey technique involves the use of structured questions to assess and report people’s beliefs, attitudes and behaviour. Collected data can be a complete enumeration of the elements of a population or a sub-group or sample of the elements of the population selected for participation in the study (Malhotra & Birks, 2007). Berg (2001) argued that the logic of using a sample of subjects for a study is that the smaller sample has the ability to make an inference about the larger population. This, in effect, reduces the cost and/or amount of work that would have been involved in studying the whole target population. There are broadly two types of survey samples, probability samples and non-probability samples. The respondents engaged in this study were sampled by convenience. Convenience sampling is a type of non-probability sampling which involves the sample being drawn from that part of the population that is close at hand. Moreover, some writers have criticized this form of data collection 41 University of Ghana http://ugspace.ug.edu.gh as having the ability to suffer from bias and arbitrariness of the researcher. This is based on the fact that selection of respondent is based on the researchers judgement and not by chance selection (Malhotra & Birk, 2006). The study used consumers of the mobile money service to study the factors that influence the adoption of mobile money in Ghana. However, due to the large population size of consumers, the study employed a mathematical formula to determine an ideal sample size in order to determine the appropriate sample for the study. The ideal sample for infinite populations as outlined by (Cochran, 1977) is given as: z 2  p(1 p) n0  , Where: e2  no is the sample size  Z is the two tailed area under the normal curve where α = 0.05 and the z value is 1.96  e is the acceptable sampling error  p is the population of a proportion with a desired attribute (assumed to be 0.5 which maximises the sample size to be determined) Given these values and an acceptable sampling error of 6.5%, the sample size is determined as: 1.96 2  0.5(1 0.5) n0  0.060 2 42 University of Ghana http://ugspace.ug.edu.gh This gives the acceptable sample size to be approximately 267. Therefore, a convenient sample of 267 respondents were selected for the quantitative study. The researcher’s strategy was to ask all respondents a primary question to know whether used mobile money services. Respondent that do not use mobile money were asked no further questions, hence, leading to some form of randomization in actual selection of respondents used for the study even though the initial selection of respondents was by convenience sampling. The researcher administered the questionnaire randomly to mainly students. The researcher settled on 300 questionnaires, having in mind that the target population as individual consumers of mobile money services. It is important to state that there was no question asked about the nationality of the respondents. Hence, it is possible to state that the respondents might include other nationals who reside in Ghana. 4.5.1 Data Types and Sources The data used for this study was primarily from primary data sources. Moreover, the findings gathered after the analysis of the primary data was further confirmed with information gathered from secondary sources in order to substantiate our claims. Neumann (2006) has posited that primary data is gathered in response to a specific research problem through the use of questionnaires, interviews or observations. The self-administered multiple choice and short answer questionnaires were distributed to respondents who have an experience in the use of mobile money, moreover, the participation of the respondents was on a voluntarily basis. Respondents were initially interviewed by asking specific questions to know whether they fall within the required spectrum of respondents before the questionnaires were administered to them. Snowball sampling 43 University of Ghana http://ugspace.ug.edu.gh was used to gain participants who were encouraged to recommend others to participate; together with convenience sampling to gain more participants. For instance, respondents were encouraged and requested to refer the questionnaires to others who were interested in the field. Sue and Ritter (2007), have established that this is an effective method of administering a survey to a large sample of a population. 4.6 Research design Research design is defined as the procedure(s) for collecting, analysing, interpreting and reporting data in research study (Creswell &Plano Clark,2007). It can also be considered as the overall strategy or blueprint underpinning the research (Ghauri & Gronhaug, 2005; Malholtra, 2006). According to (Rowley, 2002), design preferences for research is based on the aims and purpose of the research prepositions, the degree of knowledge and research viewpoint while Saunders et al. (2007) stated that research design offers the constructs for collecting and assessing data needed for research. In addition, Remenyi (2002) stated that management researchers tend to struggle when trying to choose a suitable technique and strategy. Research designs are placed into various categories such as explanatory, descriptive and exploratory research (Ghauri and Gronhaug, 2002; cited in Jancowicz, 2005). Broadly, research design could be divided into two groups; exploratory design and conclusive design. Whereas exploratory design can either be quantitative or qualitative in nature, conclusive research design constitutes either a descriptive or causal research. 4.6.1 Explorative research Exploratory research is mostly used when marketing research areas are inherently difficult to measure, especially in a quantitative manner (Malhotra & Birks, 2007). It may also be used in 44 University of Ghana http://ugspace.ug.edu.gh areas where it is necessary to define the problem more precisely, identify relevant courses of action, or gain additional insights before going on to confirm findings using a conclusive research design (Malhotra & Birks, 2007). The exploratory research design is seen as being versatile and flexible in respect to the methods being used. The marketing researcher responds to ideas and insights from the research object and may change the research techniques according to what type of information he is aiming to get. Exploratory research is used when little is known about the subject or problem which want to be analyzed, general when the problem needs to be defined more precisely and when the subject of the study cannot be measured in a structured manner (Malhotra & Birks, 2007). It is not necessary to always begin with exploratory research. 4.7 Data Collection Methods Based on the purpose of this study, a non-contrived research setting was adopted. Data were collected from people who were familiar with the use of Mobile money. Hence, making it possible for the gathering of information from the natural setting in order to minimize the influence of the researcher. With the sole aim of looking at the factors that influence mobile money adoption in Ghana, the study adopted a one shot or cross-dimensional form of study that allows for the gathering of data once. In essence, data are gathered during a period of days, weeks and months and were not revisited. However, it is important to emphasize that for the purpose of collecting useful data and information required to answer the research questions of the study (Creswell & Clark, 2007), the process of data collection used for this study went through three distinct steps: survey instrument design; selecting an appropriate sampling frame; and conducting survey with selected customers of the banks from the sampling frame. Since the positivist paradigm and the 45 University of Ghana http://ugspace.ug.edu.gh quantitative research approach was adopted for the study, using questionnaires for this survey research was deemed as the most appropriate data collection method for this study. Questionnaires: The use of questionnaires in a research study helps a researcher explore the views of a large number of people (Stroh, 2000). Questionnaires contain the survey questions that respondents are required to address. Therefore, items and questions on questionnaires must be clear enough and must seek no further clarification and assistance in answering them. There was a face to face administering of questionnaires to the respondents. Contact addresses of respondents who failed to immediately fill out the questionnaires were taken and they were contacted later for the questionnaires. Appendix II depicts a copy of the questionnaire used for the study. For the purpose of insightfully understanding the factors that affect the adoption of mobile money in Ghana, a total of 300 questionnaires were used for the study. The questionnaires were adapted from the instrument used in information systems studies (AbuShanab et al., 2010; Al-Somali et al., 2009; Alalwan et al., 2014). The items on the questionnaires were mostly closed-ended questions with a few open-ended question. The questionnaires were distributed to 300 respondents made up of students; however, only 284 were received. After analyzing those received from missing data and incomplete responses, it was realized that a total of 273 data sets were available for entry into the Statistical Package for Social Sciences (SPSS) for analysis. 4.8 Data Analysis After the collection of the quantitative survey data, the researcher proceeded to organize and summarize the collected data based on the variables selected from existing literature and according 46 University of Ghana http://ugspace.ug.edu.gh to the research questions. As posited by Clark and Creswell (2011), data analysis refers to the process of probing, cleaning, metamorphosing and modelling collected data into meaningful information that provides an adequate response to the research question. In view of this, the study analyses data collected from respondents and compares the findings based on the research questions and the selected variables. The raw data from the personally administered survey were first edited for non-answered questions. After the editing the raw data of the consumers of the banks were coded and entered into different data files. The coded database was then analyzed using SPSS 2.0 for windows. However, for the purpose of detecting any coding error the researcher employed the use of the frequencies command function in SPSS. Hence, this led to the re-coding and transformation of data into different types of variables. Data analysis consists of multiple facets and encompasses approaches that help describe facts, detect patterns, develops explanations and tests hypotheses (Berkowitz, 1997). Based on this, other techniques were adopted to help better describe the facts, detect patterns, develop explanations and test hypothesis of the collected data. 4.8.1 Structural Equation Modelling (SEM) The Structural Equation Modelling (SEM) technique is a family of widely used techniques for multivariate data analysis that seeks to explain the relationship among multiple variables (Hair, Babin, Anderson, & Tatham, 2006). SEM consists of several multiple regression models that can act as a response variable in one instance and a predictor variable in another instance. Therefore, SEM can be said to be comparable with other common quantitative methods, such as correlation, multiple regression and analysis of variance (ANOVA), as well as factor analysis and multivariate analysis of variance (MANOVA) (Weston & Gore, 2006). However, the Structural Equation Modelling technique helps to simultaneously evaluate more than one regression models. 47 University of Ghana http://ugspace.ug.edu.gh According to Hair, et al. (2006), it is possible to make empirical inferences of causation using SEM when the hypothesized relationship has strong theoretical support. Therefore, since there exists a lot of literature on established theories and hypothesis in the area of Information systems studies that helps in making inferences, SEM is seen as most useful in assessing the soundness of the causal relationships formulated based on the theory (Tobbin & Kuwornu, 2011; Toma, McVittie, Hubbard, & Stott, 2011). Although a multiple regression is noted for the assessment of cause and effect of variables and the assessment of the degree of association between variables, one major limitation of the multiple regression model is its inability to handle more than a single dependent variable at a time. However, unlike the multiple regression, using the SEM enables a researcher to handle more than a single dependent variable at a time. It bears all the capabilities of the multiple regression analysis. Therefore, it is able to help test the significance of a model, determine the error terms, and provide standardized and unstandardized coefficients. Furthermore, for the purpose of accessing measurement errors and composite reliability of estimates, using SEM is seen as an appropriate tool since it is incorporated with the Confirmatory Factor Analysis. Finally, the SEM is made up of an interactive graphical user interface which makes viewing of items and the relationship of variables easier. In view of this, the Structural Equation Modelling technique is regarded as having certain advantages that distinguishes it from other quantitative and multivariate techniques. Applying the SEM–based procedure helps in the generation of some form of substantial advantages over other first-generation techniques due to its greater flexibility to: a. Model relationships among multiple predictors and criterion variables; 48 University of Ghana http://ugspace.ug.edu.gh b. Construct unobservable variables (also known as latent variables; these represent multidimensional constructs); c. Model errors in measurement for observed variables; and d. Statistically test a priori substantive/theoretical and measurement assumptions against empirical data (i.e. confirmatory analysis). SEM involves generalization and extensions of first-generation procedures (Chin, 1998). It is also the only multivariate technique that allows for the simultaneous estimation of multiple equations (Hair, et al., 2006). In this study, the researcher employed the use of the SEM technique to estimate the relationships that were hypothesized to exist among performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention and the various items employed in measuring them. 4.9 Constructs’ Measurement The differences that exist between the term ‘construct’ and the term ‘variable’ are related to the measurement. This implies that by the usage of an actual measure such as marks on a scale, the operational definition of the construct turns it into a variable (Ghauri, Elg, & Sinkovics, 2004). By looking at behavioural dimensions or properties donated by a concept and translating it into observable and measurable elements in order to develop an index of measurement, a concept is operationalized to become measurable (Sekaran, 2003). In measuring the items of performance expectancy, effort expectancy, social influence, facilitating conditions hedonic motivation, price value and behavioural intentions towards the target behaviour of mobile money adoption, the 49 University of Ghana http://ugspace.ug.edu.gh operational constructs for the proposed research framework specified by Venkatesh et al.’s (2012) work was adapted with some adjustment. 4.10 Quality Standards for Research: Validity and Reliability 4.10.1 Reliability The assessment of the degree of consistency between multiple measurements of a construct is referred to as reliability (Hair, et al., 2006). In essence, reliability is a measure of the stability of the proposed measure (Ghauri & Gronhaug, 2005). Based on this, consistency of items within a measure and stability of the measure over time are deemed the two basic concerns that are addressed with respect to reliability of a measure. Reliability assessment is therefore conducted with approaches such as test-retest, alternative forms and internal consistency (Malhotra & Birks, 2007). However, Hair, et al. (2006) have posited that the commonly used approach for assessing reliability is internal consistency. Internal consistency is measured using the split half reliability, which is mostly measured by using coefficient alpha and Cronbach alpha (Hair, et al., 2006; Malhotra & Birks, 2007). 4.10.2 Validity In order for a measurement scale to be used confidently, the scale must possess some level of validity. This implies that a measurement scale ought to measure what it purports to measure. In view of this, validity can be defined as the extent to which a scale or set of measures accurately represents the concept of interest (Hair, et al., 2006). Therefore, types of validity used for this study includes convergent validity, construct validity, and divergent validity (Malhotra & Birks, 2007). 50 University of Ghana http://ugspace.ug.edu.gh 4.11 Summary An outline of the research methodology used for answering the research questions stated at the beginning of the study was presented in this chapter with attention given to the research paradigm, research method, sampling techniques, data collection and method of analysis. The positivist paradigm was selected after a careful consideration of the research problem, the research purpose, the objective of the research and the research questions. For the main aim of accurately answering the research questions an exploratory method of research and a quantitative approach was used. The data used for the study was primary in nature and the data analysis technique adopted was the multivariate technique in order to ensure that validity reliability was created; hence, enabling the assurance of the necessary validity reliability creation in the study. 51 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE ANALYSIS AND FINDINGS 5.1 INTRODUCTION This chapter presents the assessment and testing of the proposed research model using Structural Equation Modelling. Based on this, the chapter will focus mainly on the demographic characteristics of the respondents, data screening, and the analysis of the data collected through the survey instrument. The process of analyses in this section is therefore categorized into three main parts. The first part, which focuses on the description of the demographic characteristics of the respondents delves into issues relating to gender, age, marital status and educational level, occupation. This is done in order to assess the respondents used for the study. The second part involved the assessment of the research model proposed in Chapter 3 of the study using the Structural Equation Modelling analysis to test the model fit and validity based on satisfactory results. The final part then proceeds to test the research hypothesis formulated in the third chapter of the study. A combination of the results from the model validating and the hypothesis testing ensured the determination of the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value on consumers' adoption of mobile money in Ghana and the moderating effect of gender, age and education on the proposed model. This, in effect, leads to the answering of the first and second reseach questions and agrees with the assertion by Hair, et al. (2006) that the combination of the two approaches helps assure that good construct measures are represented in the valid structural model. 52 University of Ghana http://ugspace.ug.edu.gh 5.2 Data Screening Data examination is the process of assessing a data for a research for missing data, outlier, and normality assumption response to determine the readiness of the dataset for further statistical analysis. Missing data referred to a situation where items on a survey were not answered by respondents either intentional or unintentional (Hair, Black, Babin, & Anderson, 2010). Extreme data values with a unique combination of attributes that are different from other data values are referred to as Outliers (Hair et al., 2010). According Byrne (2010), an outlier may have significant effect on the consequent model, fit, parameter estimates and standard errors in the dataset. Normality on the other hand, refers to the normal distribution of the dataset. Hair et al., (2010) asserted that, estimating of maximum likelihood in Structure Equation Modelling (SEM) and Logistic regression demands, the dataset be normally distributed. Kurtosis and Skewness are two methods of checking for normality even though histograms can be used as well. Skewness measure of the extent to which a distribution of a dataset deviates from the mean the while Kurtosis measure of the flatness of the distribution in the dataset (George & Mallery, 2005). The rule of thumb is that, the values of Kurtosis and Skewness of the variables fall between -2 and +2 to achieve normality however, for large sample above 200, the effect of the non-normality of data may be negligible on the result (Hair et al., 2010). From the data collected, some responses had missing data hence, the missing data was imputed by MCAR test in SPSS 20 (Rubin, 1996). Also, five responses were found to be providing same answers to all question regarding adoption of Cloud computing in Section C of the research questionnaire hence, a bias was reflected in these responses. According to Gaskin (2013), these bias responses will have little impact on the study hence, should be delete from the dataset. In all 53 University of Ghana http://ugspace.ug.edu.gh 299 responses were used in the further analysis. The normality test was conducted in Amos 22 and the result is presented in Appendix D. 5.3 Sampled Characteristics 5.2.1 Demographic Profile of Respondents This section discusses the demographic profile of the sampled respondents who took part in the study. They have been profiled in according to their gender, age, and educational qualification. Results from the demographic data of the sampled respondent reveal that there were 186 males signifying 64% and 104 females signifying 36% of the total number of respondents. Majority of the respondent were within the age group of 18-24 with 56.7%, 31% were between the age group of 25-30. 6.5% respondents were less than 18, and 2.6% respondents were within the age groups of 31-35 and 36-40 respectively. In terms of educational level of the respondents as of the time of study, most of them were studying for their bachelor’s degree making 49.5% of the total number of respondents, masters students were 32.2%, 16.3% were Senior High School students whereas professional students were 2%. 5.1 Measurement Model Assessment and Confirmatory Factor Analysis (CFA) Under Structural Equation Modelling (SEM) data analysis, the measurement model is an essential tool for the assessment of the validity of the model for further analysis. Exploratory factor analysis, confirmatory factor analysis and a hybrid of both approaches called the hybrid approach are the three main approaches used for model assessment (Ahire & Devaraj, 2001). Although the two approaches are helpful for model assessment, the exploratory factor analysis (EFA) approach extracts factors based on statistical results not on theory and can be conducted without prior 54 University of Ghana http://ugspace.ug.edu.gh knowledge of the number of factors or which items belong to the construct. However, with the confirmatory factor analysis (CFA) approach, both the number of factors within the set of variables and the factors that each item loads highly on, is made known to the researcher before results are computed (Hair, et al., 2006). In view of this, CFA as a tool enables a researcher to either confirm or reject a preconceived theory. Thus, it can be said that CFA provides an assessment of fit whilst EFA does not. Moreover, with regards to detecting uni-dimensionality issues and multi- dimensionality sets within construct measurement, using EFA is deemed as far better when compared to CFA which is only capable of detecting uni-dimensionality problems without indicating the dimensions. On the basis of this, the current study has chosen to apply the CFA approach as the method needed for confirming a good representation of the construct of the conceptual model by the proposed items or indicators through validation of the measurement model (Hair, et al., 2006). The CFA conducted on a measurement model is usually made up of a series of steps that assigns items to their respective latent variables with the appropriate error terms. The steps involved in a model assessment using CFA in a study includes model specification, iterative model modification and estimates of Goodness of Fit (GOF) statistics using Amos 20.0. The 28 latent variables were specified with six items measuring them but these 28 variable themselves belonged to seven different constructs, namely: performance expectancy (PE); effort expectancy (EE); social influence (SI); facilitating conditions (FC); hedonic motivation and behavioural intention (BI). As showed in Figure 5.1 below, the CFA was run with all the variables of the measurement models linked together by labels that match statements 1-28 on the Likert scale. The next stage of the study was to test for the validity of the measurement model under the second order CFA to determine 55 University of Ghana http://ugspace.ug.edu.gh the convergent and discriminant validity. These two forms of validation were tested in an iterative model modification process of refinement and testing. Then the second stage of structural model testing was carried out. Figure 5. 1 Measurement Model for Mobile Money Usage 56 University of Ghana http://ugspace.ug.edu.gh 5.2 Measurement Model By employing the CFA with the maximum likelihood (ML) estimates using AMOS 2.0 the initial fitness indices indicated that the model had a poor fit due to the CMIN = 624.445, DF = 329, P- value = 0.000. Moreover, some authors have argued that, it is misleading to rely on only Chi- square statistics for assessing model specification (Byrne, 2013, Schumacker & Lomax, 2004, Hair, 2010). Hence, further test used in assessing GOF was introduced. The result revealed that most of the values had poor fit as indicated by CMIN/DF = 1.898, GFI = 0.865, RMSEA = 0.057 with 90% confident interval (0.051, 0.064), RMR = 0.051, CFI = 0.935, NFI = 0.873, TLI = 0.925, IFI = 0.935, AGFI = 0.833, PCFI = 0.814, and PNFI = 0.760. Accordingly, this necessitated purification and re-analysis of the measurement model (Anderson & Gerbing, 1988). By looking at the modification indices, standardized regression weight (factor loadings), square multiple correlation and standardized residual covariance as listed in the AMOS output, this study was able to identify the problematic items that decreased the model fit. In light of this, 2 items of the facilitating condition variable and one item of the behavioral intention variable was deleted. The result of a re-run after the deletion of the items showed that the modified model had a good fit with observed data, hence further modification was not needed. The result is therefore depicted Table 5. 1 below. Table 5. 1 Goodness of Fit Indices for Measurement Model Fit Indices Accepted Value Model Value Absolute Measure Chi-square 458.392 Degree of Freedom 254 CMIN/DF ≤ 2 1.805 Probability P ≥ 0.05 0.000 57 University of Ghana http://ugspace.ug.edu.gh RMSEA (Root Mean Square Error of ≤ 0.08 0.054 Approximation) RMR 0.048 Incremental Fit Measures NFI (Normed Fit Index) ≥ 0.90 0.901 CFI (Comparative Fit Index) ≥ 0.90 0.953 TLI (Tucker-Lewis Index) ≥ 0.90 0.944 IFI (Incremental Fit Index) ≥ 0.90 0.953 Parsimony Fit Measures AGFI (Adjusted Goodness of Fit Index) ≥ 0.80 0.851 PCFI (Parsimony Comparative of Fit Index) ≥ 0.50 0.807 PNFI (Parsimony Normed Fit Index) ≥ 0.50 0.762 5.3 Instrument Validation In regard to instrument validation, confirmatory factor analysis (CFA) was used in verification of convergent validity and discriminant validity. Anderson and Gerbing (1998) suggest that an instrument that passes the CFA based on-factor loading test reaches the significant level (p<.05) via the model’s convergent validity verification. The results show that all items reached the significant level, reflecting that the items belonging to each factor are valid in measuring the same concept (see Table 1). The composite reliability (CR) and average variance extracted (AVE) were used to test the validity and reliability of the instrument as well (Lee, Cheung & Chen, 2007). According to Bagozzi and Yi’s (1998) recommendations, the CR and AVE values should be greater than .50 in order to demonstrate that the item quality is acceptable. The test results show that all but one factor passed the standard (see Table 5. 2). 58 University of Ghana http://ugspace.ug.edu.gh Table 5. 2 Construct Reliability Results for the Pooled Data File Construct Items Factor AVE Composite Cronbach Loadings Reliability Alpha TPE PE1 0.815 PE2 0.864 PE3 0.888 PE4 0.804 0.711 0.908 0.906 TEE EE5 0.839 EE6 0.863 EE7 0.864 EE8 0.835 0.735 0.917 0.917 TSI SI9 0.788 SI10 0.839 SI11 0.798 SI12 0.746 SI13 0.643 0.586 0.875 0.873 TPC PC14 0.877 PC15 0.876 PC16 0.703 PC17 0.761 0.653 0.882 0.882 TFC FC18 0.749 FC19 0.816 0.643 0.779 0.755 59 University of Ghana http://ugspace.ug.edu.gh TBI BI22 0.660 BI23 0.664 BI24 0.867 BI25 0.905 0.614 0.862 0.854 TUB UB27 0.905 UB28 0.965 0.741 0.851 0.846 Discriminant validity also refers to the extent to which a construct is distinct from other constructs (Hair, et al., 2006). There are usually different ways of calculating discriminant validity. A conservative approach is the comparison of the average variance estimated (AVE) by a construct’s scale items with a square inter-scale correlation for that construct. In an event where the AVE is consistently higher than the square inter-scale correlation of the construct, discriminant validity is supported (Hair, et al., 2006). Based on this assertion, it is important to state that the discriminate validity of the study was therefore supported. 5.4 Assessing the Structural Model The assessment of the structural model in Amos involves the determination of whether the specified theoretical relationships in the model are indeed supported by data (Cobb, 2007). Based on this, the assessment of the structural model in this study was to determine whether the relationships hypothesised based on theory were supported by data. The fitness of the structural model was, however, tested using the various fit indices. The chi-squared (χ²) test was used to assess the exact model fit for the study. Moreover, other appropriate approaches that helps in 60 University of Ghana http://ugspace.ug.edu.gh determining model fit were also examined to provide additional information on model fit and the indices ranged from good to very good (Bagozzi & Yi, 2012). The study estimated a full measurement model whereby all items were entered simultaneously to predict the measurement model. 61 University of Ghana http://ugspace.ug.edu.gh Figure 5. 2 Structural Model 62 University of Ghana http://ugspace.ug.edu.gh 5.5 Structural Equational Modelling After an assessment of the measurement model fit and construct validity, the next step was to assess the structural model. This involved the testing of the hypothesised theoretical model to ascertain the relationship between the latent constructs. The result revealed that the strongest path coefficient existed between Price value and Behavioural intention, whilst the weakest one was between facilitating conditions and behavioural intention. Hence, except for the research hypothesis H5, hypothesis H1, H2, H3, H4, H6 and H7 were all supported. Table 5. 3 Structural Path Analysis Estimate S.E. C.R. P H Path H1 TPE TBI 0.172 0.112 2.598 0.009 H2 TEE TBI 0.232 0.134 2.499 0.012 H3 TSI TBI 0.150 0.097 2.412 0.016 H4 THM TBI 0.200 0.147 2.207 0.027 H5 TFC TBI 0.000 0.025 0.020 0.984 H6 TPV TBI 0.142 0.023 5.140 *** 63 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX DISCUSSIONS OF RESULTS 6.1 INTRODUCTION An analysis of the findings in relation to the objectives of the research was in the preceding chapter sought to give an analysis of the empirical findings in relation to the objectives in the introductory chapter. This chapter discusses hypotheses testing results and findings with respect to the constructs of the UTAUT 2 model. Therefore, the current chapter focuses on exploring the influence of performance expectancy, effort expectancy, social influence, hedonic motivation and price value on behavioural intention and the influence of facilitating conditions and behavioural intention on actual usage of mobile money services in Ghana. This chapter, then, concludes by addressing the fulfilment of the research objectives. 6.2 The UTAUT 2 Model This section addresses the first research question as stated in Chapter One. Extant research without any doubt exists on mobile money research worldwide but arguably the reasons behind the change in adoption behaviours in developing countries, especially in Ghana, has not gained an eclectic audience by researchers. The UTAUT 2 model was empirically tested using data collected from respondents in Ghana. The findings provided total support for the model except for one construct. The analysis of the study proved that performance expectancy, effort expectancy, social influence, hedonic motivation and price value had a statistically significant influence on users’ behavioural intention to adopt mobile money, whilst behavioural intention on the other hand, statistically supported the usage behaviour of consumers. Only facilitating conditions received a negative correlation with behavioural intention. It is however important to 64 University of Ghana http://ugspace.ug.edu.gh note that the findings have highlighted some aspects of the constructs that have not been measured by most existing studies. 6.2.1 Performance Expectancy The performance expectancy construct again is the degree to which using a technology will provide benefits to consumers in performing certain activities. It was revealed that mobile money users in most developing countries feel the need to adopt mobile money services in order to create added value to their businesses due to the dynamic prospects mobile money services offer. In this study, the performance expectancy construct positively contributed to explaining the variance in behavioural intention. This implies that consumers who have high performance expectancy are likely to have the intention to use mobile money services. The result of this study is therefore in support with some studies such as Thakur & Srivastava (2013), Slade et al. (2014), Venkatesh et al. (2011), who after employing UTAUT and UTAUT 2 found performance expectancy to be positive. 6.2.2 Effort Expectancy This construct is associated with ease associated with consumers’ use of technology. Its relationship between effort expectancy and behavioural intention was also supported positively. This means that consumers’ intention to use mobile money is dependent on greater effort expectancy. Although this study reveals that most respondents were offering their bachelor’s degree at the time, most of the respondents admitted to the fact that mobile money was easy to use. The result is in line with the assertion by extant researchers such as Thakur & Srivastava (2013), 65 University of Ghana http://ugspace.ug.edu.gh Mortimer et al., (2015) and Bampoe (2015) that effort expectancy has a higher influence on mobile money adoption. 6.2.3 Social Influence The social influence variable refers to the extent to which consumers perceive that important others (e.g., family and friends) believe they should use a particular technology (Venkatesh et al., 2003). Previous research has found that social influence is a positive predictor of consumers’ intention to adopt mobile money. Slade et al. (2014), Zainudeen (2011), Bampoe (2015) empirically found that individual intention to use mobile money was significantly affected or influenced by friends and family members. Therefore, the current study reported that social influence positively contributes significantly towards explaining the variance in behavioural intention. Hence, the significant effect of the social influence on behavioural intention is a clear indication that the respondent used for the study are concerned about environmental factors such as the opinion of friends. Koenig-Lewis, Marquet, Palmer, & Zhao (2015) stated that the social environment of the adopter plays an important role in the adoption process of mobile money and thus promoting mobile money services via word of mouth from opinion leaders is crucial for a faster diffusion of these technologies. 6.2.4 Hedonic Motivation Hedonic motivation is the pleasure acquired from the use of technology. Hedonic motivation was also found to be positively related to behavioural intention. It was posited by Venkatesh et al. (2012) that this construct will affect behavioural intention due to differences in consumers’ innovativeness, novelty seeking, and perceptions of novelty of a target technology. The fact that 66 University of Ghana http://ugspace.ug.edu.gh consumers find a technology has innovativeness and dynamism will influence curiosity and intention. Again, Venkatesh et al. (2011) Zarmpou, Saprikis, Markos, & Vlachopoulou (2012) and Huang & Kao (2015) are some studies which found a positive relationship between hedonic motivation and behavioural intention. 6.2.5 Facilitating Conditions Facilitating conditions refer to consumers’ perceptions of the resources and support available to perform a behaviour or the degree to which an individual perceives that an organizational and technical infrastructure is available to support the use of a system (Venkatesh et al., 2007). It focuses mainly on the role external factors play directly on the actual usage behaviour mobile money without the arbitrating role of behavioural intention (Venkatesh et al., 2003). The external indicators of facilitating conditions can include the economy, society, policy and access. The results of this study indicated that the effect of facilitating conditions construct over usage was not supported. Therefore, this suggests that the surrounding environment of our respondents indeed influences their usage of mobile money. This result is in contrast with some existing studies such as Venkatesh et al. (2011), Mukherjee (2015), Thakur (2013), Slade & Williams (2013), and Bampoe (2015). 6.2.6 Price Value Price Value is defined as consumers’ cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them. This implies that when the benefits of using a technology are perceived to be greater than the monetary cost, price value has a positive impact 67 University of Ghana http://ugspace.ug.edu.gh on intention. In this study, the relationship between price value was also positively supported. It accounted for the largest unique contribution in explaining the variance in behavioural intention. Thus, price value is a predictor of behavioral intention to use a technology as suggested by studies such as Venkatesh et al. (2011) and Slade & Williams (2013). 6.2.7 Behavioural Intention Following (Venkatesh et al., 2003), the relationship between behavioural intention and actual usage behaviour was supported. The study therefore revealed that behavioural intention accounted for a unique contribution to explaining the variance in usage behaviour. In effect, mobile money adopters are more likely to use the system provided they had the intention. This is to say that customers with high behavioural intention to use the system had high usage behaviour. It is also important to point out that the respondents used for the study were actual users of mobile money, therefore, it is assumed that they need not to reconsider their intention to reuse the system, since it has become an integral part of their money transfer routine. This result is in support with studies from Venkatesh et al., (2003) and Ally & Gardiner (2012). 6.3 Determinants of Mobile Money Adoption This section addresses the second research question as stated in Chapter One. The demographic profile of the sampled respondents was profiled according to their gender, age, and educational qualification. Results from the demographic data of the sampled respondent reveal that there were 186 males signifying 64% and 104 females signifying 36% of the total number of respondents. This indicates that more male than females have the tendency to use adopt mobile 68 University of Ghana http://ugspace.ug.edu.gh technologies. Majority of the respondent were within the age group of 18-24 with 56.7%, 31% were between the age group of 25-30. 6.5% respondents were less than 18, and 2.6% respondents were within the age groups of 31-35 and 36-40 respectively. This suggested also that the younger generations were active adopters of mobile money. With respect to the level of education of the respondents as of the time of study, most of them were studying for their bachelor’s degree making 49.5% of the total number of respondents, postgraduate students were 32.2%, 16.3% were Senior High School students whereas professional students were 2% also indicating that university students belong to the dominant group of the key determinants of mobile money adoption. 6.4 Summary This chapter discussed the relationships that existed between the proposed hypotheses and its influence on mobile money adoption in Ghana. The objective of this research was to determine the extent to which the hypothesis influenced consumers’ adoption of mobile money in Ghana. The concluding part of this chapter investigated the determinants of adoption such as gender, age and education in Ghana in order to satisfy the second research question. A conclusion is made to the fact that indeed all seven constructs of the UTAUT 2 model had positive relationships with use and intention. 69 University of Ghana http://ugspace.ug.edu.gh CHAPTER SEVEN CONCLUSIONS, IMPLICATIONS AND RECOMMENDATIONS 7.1 INTRODUCTION The analysis of the empirical findings and the research questions in relation to the research were discussed in the earlier chapter. This chapter seeks to concentrate on concluding the research by reviewing the main captions of preceding chapters as well as giving summary of the major findings based on the objective of the research. 7.2 Review of Previous Chapters Chapter One provided an introduction to the research. This was done by giving a brief background of the study; by pinpointing issues, theories, methods, and context gaps in the expanse of the research in order to establish the basis of the research. Furthermore, the propose of the research was clearly stated here which was to explore the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention, hedonic motivation, price value and habit on consumers' adoption of mobile money in Ghana. Out of which the research questions; 1. What is the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention hedonic motivation, price value and habit on consumers' adoption of mobile money in Ghana? 2. What are the determinants of mobile money adoption in Ghana? Were set out to help in systematically addressing the research topic and clearly spell out the purpose and objectives of the study. 70 University of Ghana http://ugspace.ug.edu.gh Further in Chapter two, the primary focus of was to examine the extant literature on mobile money adoption in order to identify gaps that have been identified within the course of the research process enabling the unearthing of the existing body of knowledge and contributions of the phenomena.. Chapter Three discussed the theoretical foundation of the study and sort to provided a justification for the model adopted for the study based on literature. In this chapter justification of the choice of theory as the theoretical lens is also addressed and hypotheses are developed. Moving on in Chapter Four the chapter discussed the general philosophical assumptions and methodologies and those employed in the study. This chapter further discussed the reasons for the choice of paradigm and methodology, data collection methods, instruments used as well as the method of analysis. Chapter Five presented an overview findings as evident from the data collection. It also discussed the demographic characteristics of the respondents used for the study and presents descriptive statistics of the scales used in measuring the constructs. The chapter also deals with validity checks through confirmatory factor analysis and presents an analysis of the relationship of the measured variables. Chapter Six discussed the analysis of the findings relative to the data collection. The discussions were based on the relationship that exists between the endogenous and exogenous variables of the model; followed by discussions to check the effect of moderators such as gender, age and education on the exogenous variables in predicting the behaviour. 71 University of Ghana http://ugspace.ug.edu.gh 7.3 Implication for Research Research on mobile money adoption has been pinpointed as an area with prominsing future, especially from the acadenmic and practicional engagement perspective. However, as argued earlier, most literature on mobile money adoption tends to have been silent when addressing salient factors that influence the adoption of mobile money such as social influence, facilitating conditions, hedonic motivation, price value and the relationship between behavioural intention and usage behaviour as well as the moderating effect of demographics such as gender, age and education. This research has therefore answered this call and opened more prospects for further research with its findings. Also, the current work is one of the few works to have utilised the Structural Equation Model analyical technique that permits a concurrent assessment of adequacy of the measurement model and the conceptual model used to assess the factors that influence the adoption of mobile money in Ghana. For instance, the study employed the use of the confirmatory factor analysis to validate the measurement model in the proposed research model. Another important contribution of this study lies in the fact that the study used an advanced technique which studies have only recently began using to examine invariance. In essence, this work used the mean and covariance structure analysis, which is an advanced technique for answering questions relating to group comparison or differences among cultures and demographics such as gender, age and educational level. Based on this, the current study used two different types of group analysis to examine the moderating effect of gender, age and education on the research model with regards to the mobile money adoption in Ghana. 72 University of Ghana http://ugspace.ug.edu.gh In exploring the factors that influrnce mobile money adoption, there seems to be some mileage in the use of the UTAUT 2 model. However, the UTAUT 2 model, although proven to be stronger than other competing models such as TAM and TPB, it is important to state that only a few UTAUT-based research exist, paricularly compared to the huge TAM/TPB-based research. Therefore, in using the UTAUT 2 model, this study have provided a unique insight on the behavioural pattern of mobile money users from a developing country context of Ghana. This is therefore an answer to the call by Venkatesh & Zhang, (2010) who have adomonised future studies to examine the generalisability and validity of the UTAUT 2 model in various technological contexts and demands. Finally, though not exhaustive, the attempt by this study to use the UTAUT 2 model to study mobile money adoption in Ghana has yielded serveral important implications and reasonable insights that can guide researchers in future research. 7.4 Implications for Practice and Policy Based on the framework used for this study, the research identified factors that influence the adoption of mobile money. From the study, findings suggested that users or subscribers’ behavioral intention to adopt mobile money is characterised by effort expectancy. This implies that subscribers intenton to adopt mobile money is based on the ease to which the mobile money technology can be operated. In light of this, the study admonishes mobile network service providers to invest more into the mobile money technology. Furthermore, based on the other constructs of the model different findings and implications were made. The performance expectancy construct was also identified as having influence on the 73 University of Ghana http://ugspace.ug.edu.gh subscribers believe that use of the system will help in the attainment of gains in the job performance. The social influence construct perceives how significant others believe they should use a new system. It has a significant influence in explaining the variance on subscribers, intention to adopt mobile money, network service provider’s effort to advertise the benefits of mobile money can be amplified through social influence on subscribers. This study further revealed that subscribers with higher behavioural intention were more likely to adopt mobile money system. In light of this mobile network service providers are advised to implement strategies and polices that would help in influencing behavioural intention. 7.5 Research Limitations Despite the general support for the model and the interesting findings that this study has produced, it is important to acknowlegde that the study has certain limitaions. The first limitation of the study is in consensus with the insufficient literature on this study in Ghana. Researchers have not done so much study on this particular topic using the UTAUT2 model in Ghana. Again, the study reports a limitaion in respect of the sample population and the type of technology investigated. The respondents choosen for the study were mostly young and highly educated. In view of this, the behaviours of such respondents can be anticipated as being different from the average population. 74 University of Ghana http://ugspace.ug.edu.gh 7.6 Future Research Future researchers are admonished to consider adding new constructs to extend the UTAUT2 model. Furthermore, the use of moderators stated in the UTAUT2 model should be used in further research studies, as it is an issue that needs attention. Again, the current study’s result of non- equality in relation to the moderating effect of gender, age and education, suggests futher investigation for situations where gender equality might be more prevalent. Since this study explored the age and education range among students, it is important for future studies to focus on older users and potential adopters especially small scale business men and women. Finally, more studies could be conducted in order to adequately investigate the factors that influence the adoption of mobile money adoption using UTAUT 2 model by adding the construct of habit and other contextual variables. 7.7 Research Conclusion In conclusion, the study clearly shows how the objectives were realised in view of the previous detailed elaborated findings made. In light of this the study began with exploring the main factors that influence users, adoption of mobile money. During the process of research, it was noticed that few studies on the factors that influence the adoption of mobile money based on the UTAUT2 model hence few literatures to help in the research. Given the predictive power of comparing the UTAUT 2 model with the other models such as TAM, TPB, IDT and UTAUT 1, and ability of the model to identify contingencies that determine 75 University of Ghana http://ugspace.ug.edu.gh the determinants of these factors the study adapted to explore the factors that influence the mobile money adoption in Ghana. The result of the study was in accordance with the relationship between factors like performance expectancy and behavioural intention, effort expectancy and behavioural intention and social influence and behavioural intention, hedonic motivation and behavioural intention, facilitating conditions and behavioural intention, price value and behavioural intention. More interestingly, whilst the relationship between behavioural intention and usage behaviour was supported the relationship between facilitating conditions and usage behaviour was found to be insignificant. In a nutshell, observations made imply that subscribers’ or users’ perception of the ease of use of technological innovation has an influence on their usage behaviour. The perception of usefulness of the mobile money influences subscribers’ adoption of the system. The relationship between social influence and facilitating conditions was found to be insignificant hence, mobile network service providers are urged to provide incentives for subscribers since referrals from subscribers can be a strong initiative to influence subscribers adopt mobile money. Finally, this study supports the application of the UTAUT 2 model in exploring the factors and the determinants that influence mobile money adoption in Ghana. 76 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. Ahire, S., & Devaraj, S. (2001). 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This research is undertaken to determine the influence of factors like performance expectancy effort expectancy social influence and hedonic motivation, price value and behavioural intention on users’ adoption of mobile money services. Thank you for taking time to fill this questionnaire. Your willingness to participate in this research indicates a voluntary action. Your contribution is vital to the success of the research. Please take note that this exercise is entirely for academic purposes and all information will be kept strictly confidential. Thank you very much for participating in this research. For further clarifications or questions please do not hesitate to contact me via email: Researcher’s Name: Dorcas Asanoa Tandoh Email: asanoatandoh@gmail.com 92 University of Ghana http://ugspace.ug.edu.gh Survey Questionnaire Part One: Demographic information (Please tick [√]) 1. Gender: Male [ ] Female [ ] 2. Marital Status: Single [ ] Married [ ] Divorced [ ] Other [ ] 3. Age: Below 18yrs. [ ] 18-24 [ ] 25-30 [ ] 31-35 [ ] 30-40 [ ] 4. Highest level of education: No Formal Education [ ] Primary [ ] JHS [ ] SHS [ ] Diploma/HND [ ] First degree [ ] Postgraduate [ ] Ph.D. [ ] Part Two: Mobile phone or Mobile device Usage (Please tick [√]) 5. Do you own or use a mobile phone or device? Yes [ ] No [ ] 6. How long have you been using your mobile phone or device? Less than 1yr. [ ] 1- 2 yrs. [ ] More than 2 yrs. [ ] 7. Which of these is your Mobile Service Provider? (Tick as many as applicable) MTN [ ] Vodafone [ ] Airtel [ ] Tigo [ ] Glo [ ] Expresso [ ] 8. What activities do you use your mobile phone or device for? (Tick as many as applicable) To receive & make calls [ ] To send & receive SMS/Texts [ ] To chat online [ ] To listen to Radio/FM [ ] To listen to music [ ] To watch videos [ ] To download [ ] To surf the Internet [ ] To Play Games [ ] To use Mobile money services [ ] Any other uses (Please Specify): ……………………..………………………………......... Part Three: Mobile Money Usage 1. Have you ever heard of mobile money? Yes [ ] No [ ] 2. Have you ever used mobile money services? Yes [ ] No [ ] 3. If yes, which mobile money service provider do you use? (Tick as many as applicable) MTN Mobile Money [ ] Vodafone Cash [ ] Airtel Money [ ] Tigo Cash [ ] 4. How often do you use mobile money services? Daily [ ] Weekly [ ] Monthly [ ] Yearly [ ] 93 University of Ghana http://ugspace.ug.edu.gh 5. On a weekly basis, how many times do you use mobile money services? Never [ ] once a week [ ] 2-3 times [ ] more than 3 times [ ] 6. Through which means do you use mobile money services? Personally [ ] Agents [ ] Both [ ] 7. How often do you use your mobile money for the following services? Functionality Never Rarely Sometimes Often Always NA 1 2 3 4 5 - View Only Balance enquiry Receipt of deposits into wallet Recent transactions Action/Wallet Control Purchasing airtime Sending Money Receiving Money Bill Payments Cash withdrawals Depositing money into wallet Part four: Mobile Money Adoption Factors Using a rating scale from the lowest point of 1 to the highest point of 5, please circle the number that indicates your level of agreement or disagreement with the following statement. SD = strongly disagree | D = Disagree | N = Neutral | A = Agree | SA = Strongly Agree | NA= Not Applicable No Statement 94 University of Ghana http://ugspace.ug.edu.gh SD D N A SA NA Performance expectancy 1. In carrying out my tasks, I think mobile 1 2 3 4 5 - money 2. I think that using mobile money enables me 1 2 3 4 5 - conduct tasks more quickly 3. I think mobile money use has increased my 1 2 3 4 5 - productivity 4. I think my performance has improved due to 1 2 3 4 5 - the use of mobile money SD D N A SA NA Effort expectancy 5. The mobile money service platform is clear 1 2 3 4 5 - and understandable 6. It is easy for me to become competent when 1 2 3 4 5 - using mobile money 7. I find mobile money easy to use 1 2 3 4 5 - 8. I think that the mobile money process is easy 1 2 3 4 5 - for me to remember SD D N A SA NA Social Influence 9. Mobile money is a widely accepted mode of 1 2 3 4 5 - payments 10. People I conduct business with think that I 1 2 3 4 5 - should use mobile money 11. All my friends use mobile money services 1 2 3 4 5 - 12. All my family members use mobile money 1 2 3 4 5 - services SD D N A SA NA Facilitating conditions 13. I have the resources needed to use mobile 1 2 3 4 5 - money 14. I have the understanding needed to use mobile 1 2 3 4 5 - money 95 University of Ghana http://ugspace.ug.edu.gh 15. Mobile money is compatible with various 1 2 3 4 5 - mobile devices 16. Help is available when I encounter a problem 1 2 3 4 5 - using mobile money SD D N A SA NA Behavioural intention 17. I predict I would use mobile money in the 1 2 3 4 5 - next months. 18. I intend to perform a transfer on the mobile 1 2 3 4 5 - money service platform. 19. I intend to use the mobile money service 1 2 3 4 5 - more frequently for payments. 20. I intend to use any other services introduced on 1 2 3 4 5 - the platform SD D N A SA NA Hedonic Motivation 21. I am happy when I am able to send money 1 2 3 4 5 - 22. I am happy when I am able to receive money 1 2 3 4 5 - 23. I am happy when I am able to pay my bills via 1 2 3 4 5 - mobile money 24. I am happy that the mobile money platform is 1 2 3 4 5 - easy to understand SD D N A SA NA Price Value 25. Mobile money helps save time and cost of 1 2 3 4 5 - transportation 26. Resources needed to use mobile money are 1 2 3 4 5 - cost effective 27. SIM card replacement is affordable 1 2 3 4 5 - 28. Mobile money is cost efficient as compared to 1 2 3 4 5 - other payment systems 96