UNIVERSITY OF GHANA COLLEGE OF HUMANITIES CONSUMER PERCEIVED RISK AND ONLINE SHOPPING IN GHANA: THE MODERATING ROLE OF ELECTRONIC-WORD OF MOUTH BY STEPHEN CLETRON MINTAH (10599826) THESIS SUBMITTED TO THE DEPARTMENT OF MARKETING, UNIVERSITY OF GHANA BUSINESS SCHOOL, LEGON IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF MPHIL MARKETING DEGREE JULY, 2018 DECLARATION I do hereby state that this work is the outcome of my own research work. I declare that no part of this work has been presented by anyone, for any academic award in this or any other University. All references used in the work have been fully acknowledged. I fully accept responsibility for any shortcoming of this work. I bear sole responsibility for any shortcomings. ………………………………………. ...............……................ STEPHEN CLETRON MINTAH DATE (10599826) i CERTIFICATION I hereby certify that this thesis was supervised in accordance with procedures laid down by the University. …………………………………… ……………………. DR. PRINCE KODUA DATE (SUPERVISOR) …………………………………… …………………… DR. KOBBY MENSAH DATE (CO-SUPERVISOR) ii DEDICATION I dedicate this thesis to my parents, Mr. and Mrs. Mintah. Thanks for your endless love, prayers and support. iii ACKNOWLEDGEMENT My sincerest gratitude is to God for the chance to contribute to human knowledge. My sincere appreciation also goes to my principal supervisor, Dr. Prince Kodua for his constructive criticism, constant direction and guidance as well as encouragement. My second appreciation goes to Dr. Kobby Mensah for his guidance and direction which shaped this work, I say thank you very much. I also want to thank my family and those who have supported me in diverse ways during this season; especially, my sister Judith Mintah, Angela Yeboah, Esther Okyerebia Boateng and Francis Agyei. Thank you all for your support and encouragement. To all my course mates who have made this journey worthwhile, I say God make us live well with what we have received. Finally, to all who directly or indirectly helped me finish this program successfully, I say God richly bless you for supporting and encouraging me with your good counsel, prayers and patience. iv TABLE OF CONTENTS DECLARATION ........................................................................................................................... i CERTIFICATION........................................................................................................................ ii DEDICATION ............................................................................................................................. iii ACKNOWLEDGEMENT .......................................................................................................... iv TABLE OF CONTENTS ............................................................................................................. v LIST OF TABLES ..................................................................................................................... viii LIST OF FIGURES ..................................................................................................................... ix ABSTRACT…………………………………………..……………………………………………………………………………x CHAPTER ONE ........................................................................................................................... 1 INTRODUCTION ........................................................................................................................ 1 1.0 Background of the Study ...................................................................................... 1 1.1 Problem Statement ................................................................................................ 5 1.2 Research Objectives .............................................................................................. 7 1.3 Research Questions ............................................................................................... 7 1.4 Hypotheses ............................................................................................................. 8 1.5 Significance of the Study ....................................................................................... 8 1.6 Scope of the Study ................................................................................................. 9 1.7 Limitation of the Study ......................................................................................... 9 1.8 Chapter Disposition…………………...……………………………………………..10 CHAPTER TWO .............................................................................................................. 10 CONTEXT OF STUDY ............................................................................................................. 12 2.0 Introduction ........................................................................................................ 12 2.1 The service sector in Ghana ................................................................................ 12 2.2 Players of some Online Shops in Ghana ............................................................. 14 2.3 Technological Innovation and Online shopping behaviours ............................. 17 2.4 Challenges with Online Shopping Acceptance in Ghana................................... 19 CHAPTER THREE.................................................................................................................... 21 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK ............................................. 21 3.0 Introduction ........................................................................................................ 21 v 3.1 Theoretical Framework……………..………………………………………….….21 3.1.1 Technology Acceptance Model…………………………………………………………………….…….22 3.1.2 Theory of Planned Behaviour…………………………………………………….………………..……..23 3.2.2 Summary of Theoretical Framework………………………………………………………….……..…..24 3.2 Conceptual Review .............................................................................................. 25 3.2.1 The Concept of Consumer Perceived Risk…………………………………………………… . 26 3.2.2 3.2.2 Online Shopping Behaviours ................................................................. 27 3.3 Review of Related Studies……………………...……………………………………….30 3.3.1 Perceived risk factors and adoption of online shopping behaviours…………..30 3.3.2 Electronic Word Of Mouth…………………..……………………...…………… 33 3.4 Summary of Literature………………..……………………………………...………...35 Propose Model…………………………………..………………………..…………………36 CHAPTER FOUR ..................................................................................................................... 37 RESEARCH METHODOLOGY .............................................................................................. 37 4.0 Introduction ................................................................................................................... 38 4.1 Philosophical Foundations ………………………………………………………39 4.2 Research Design and Approach ..................................................................................... 40 4.3 Research Setting ............................................................................................................. 42 4.4 Selection of Participants for the Study ............................................................................ 41 4.4.1 Population of the Study ............................................................................................. 412 4.4.2 Sample Size .................................................................................................................. 422 4.4.3 Sampling Technique .................................................................................................... 432 4.5 Measures for Data Collection ...................................................................................... 452 4.6 Procedures for Data Collection ...................................................................................... 46 4.7 Ethical Consideration..................................................................................................... 47 4.8 Data Analysis .................................................................................................................. 48 vi CHAPTER FIVE ........................................................................................................................... 49 DATA PRESENTATION, ANALYSES AND DISCUSSIONS OF FINDINGS .......................... 49 5.0 Introduction .................................................................................................................... 49 5.1 Data Analysis ....................................................................................................................... 49 5.1.0 Demographic Profile of Respondents ............................................................................... 49 5.2 Online Shopping Activities............................................................................................. 51 5.3 Descriptive Statistics of Scores....................................................................................... 52 5.4 Structural Equation Modelling Procedures .................................................................. 56 5.4.0 Exploratory Factor Analysis ......................................................................................... 56 5.4.1Confirmatory Factor Analysis ....................................................................................... 60 5.4.2 Testing Hypothesized Relationships ........................................................................... 67 5.5 Observed Model ............................................................................................................. 71 5.6 Discussion of Findings .................................................................................................... 72 5.6.0 Perceived Risk Factors and Online Shopping Behaviours ........................................... 73 5.6.1 Moderation Role of E-word of Mouth .......................................................................... 74 5.7 Chapter Summary .......................................................................................................... 75 CHAPTER SIX.............................................................................................................................. 76 SUMMARY, CONCLUSION AND RECOMMENDATION ...................................................... 76 6.0 Introduction ................................................................................................................... 76 6.1 Summary of the Study………………………………………………………………………..78 6.2 Conclusions of the Study...................................................................................................... 78 6.3 Recommendations ................................................................................................................ 80 6.4 limitations of the study…………………………………………………………………..……84 6.5 Future Studies …………………………………………………………………………………85 REFERENCES .…………………………………………………………………………………… 87 vii LIST OF TABLES Table 5. 0: Demographic profiles of participants ................................................................. 50 Table 5. 1: Online shopping behaviours of participants ....................................................... 51 Table 5. 2: Descriptive Statistics of Scores .......................................................................... 52 Table 5. 3: Rotated Component Matrix Table ...................................................................... 57 Table 5. 4: Parameter estimate threshold for model fit indices ............................................. 59 Table 5. 5: Fit indices of final measurement model .............................................................. 63 Table 5. 6: Correlations for CFA and SEM Analysis ........................................................... 64 Table 5. 7: Validity and reliability of final model ................................................................ 65 Table 5. 8: Fit indices for hypothesized relationships........................................................... 68 Table 5. 9: Summary of significance of hypothesized relationships ..................................... 69 viii LIST OF FIGURES Figure 3.0: Hypothesized Model of Consumer Perceived Risk and Online Shopping Behaviours .......................................................................................................................... 35 Figure 4.0: Map of Accra Metropolis .................................................................................. 41 Figure 5.0: Final measurement model .................................................................................. 62 Figure 5.1: Observed Model of Predictors and Moderators of Online Shopping Behaviours……………………………………………………………………………………72 ix ABSTRACT Despite increase in technology and convenience associated with online shopping, the progress of acceptance has been very slow in Ghana. There is a theoretical argument that the perceived fear people associate to technological innovations serves as a barrier for it acceptance. However, in Ghana, perceived fear and online shopping adoption been under researched. The current study examined how different dimensions of consumer perceived risk influence online shopping behaviours in Ghana. A total of 200 online shoppers were conveniently selected from the Accra Metropolis in Greater Accra region of Ghana. Data was collected through cross-sectional survey and analysed using structural equation modelling. Findings showed that participants have been engaging in online shopping behaviours in the last five years, with majority of them shopping online in the last two years. Consumer perceived risk constructs had negative effect on online shopping behaviours. Specifically, financial, privacy and delivery risks had significant negative impact on online shopping behaviour. Perceived financial risk had the greatest effect on online shopping behaviours among the participants. E- word of mouth moderated the effect of perceived risk on online shopping behaviours, such that an increase in e-word of mouth reduces the negative impact of perceived risk on online shopping behaviours. The findings are discussed within the context of promoting online shopping in Ghana. x CHAPTER ONE INTRODUCTION 1.0 Background of the Study Globalization and the emergence of technological innovation have changed the business landscape. Nowadays, many businesses have sort to rely on information technology when it comes to provision of services that customers need (Arora & Rahul, 2018; Doolin, Dillon, Thompson & Corner, 2005). With the advent of internet and its related activities, organizations are putting in efforts and measures to utilize the benefits that the internet brings with it. In current times, advancement in the internet technology has offered customers wide range of choices, but also an integral way of shopping. For this, businesses have align strategies to fully take advantage of it. One of such is the selling of goods and services online known as online shopping (Chang & Wu, 2012; Faqih, 2013; Masoud, 2013). Online shopping is becoming an increasing phenomena in the business landscape over the years coupled with its benefits that comes with it such as convenience, prior online experiences (Li & Huang, 2009; Tsai & Yeh, 2010), usage usefulness and other motives such as coverage, delivery of information based products and after sales service (Hsieh & Tsao, 2014; Wu & Ke, 2015), becoming a possible threat to traditional form of shopping (Zendehdel & Paim, 2012). Hence, online shopping removes geographical barriers that traditional brick and mortar firms face. Moreover, firms adapting online platform have the ability to easily customize according to a customer’s preference (Dillon, Buchanan & Al- Otaibi, 2014) which some studies have confirmed to encourage confidence and in the long run leads to an increase in purchase intentions (Tsai & Yeh, 2010). Now, shopping online has become a viable way of purchasing products and is now posing severe competition to traditional shopping channels, in certain product areas (Dillon, et al., 2014). 1 In Ghana, the overall internet penetration rate increased from forty percent to forty-point seven percent as at the end of August, 2013(Kwarteng & Pilik, 2016). Ghana has witnessed rapid technological changes having impact on people’s lives and commerce (ISSER Report, 2007). Making it possible for organizations to infuse technological innovations into their operations. In recent times many online shopping companies have sprang-up and more developing. However, considering the population of Ghana with over 27 million (Internet World Statistics, 2017) it is expected that the numbers in online shopping should have increased proportionally to the population. Telecommunication and bank companies have been at the forefront of providing online shopping behaviours in the Ghanaian market. The challenge is that, Ghanaians perceive online shopping as risky due to what happened in 1995 a report by the US Federal Bureau Investigations about fraudulent activities by some internet users which tarnished the image of some Ghanaian online shoppers (Alemma & Ndanu 2005; Olatunji-Osei, 2010). Many people doubt the security of their accounts (Credit Cards) while shopping online, others include high cost, time loss, high levels of electronic fraud, high levels of illiteracy. This generates a situation that imitate the effort to accelerate the growth of online shopping in Ghana (Awiagah et al., 2016; Boateng, et al., 2011). Upon all the benefits, the advancement of online shopping innovation has been growing steadily, but still has not reached the high growth projections made in the later years of the 1990s (Ariff, Sylvester, Zakuan, Ismail & Ali, 2014). Research has proven that consumers used the internet for information searching than for purchasing purpose (Tandon, Kiran & Sah, 2018). This situation indicates that there have been slow adoption behaviours on the part of consumers. Apart from the slow acceptance of online shopping, people’s trust and adoption of technological innovation continues to be a concern. Some studies in the past has argued that one of the key inhibitors of technological adoption in general and online shopping 2 in particular is the perceived risks that consumers usually associate with online shopping (Amir& Rizvi, 2017; Xie, 2017). More so, previous studies reported findings to the effect that potential online shoppers perceive the act of shopping online to be very risky transaction and that the perceived risk is capable of affecting their likelihood of purchase (Amir & Rizvi, 2017). For this reason many scholars assumed that perceived risk among consumers negatively affects their online shopping behaviour (Tamatompol, Pangemanan & Pandowo, 2017), preventing many of them from online shopping. Perceived risks are the beliefs of uncertainty or doubts regarding possible negative consequences (dangers) in using a good or service (Padhi, 2017). Perceived risks that consumers associate with online shopping activities negatively impact online purchasing intention and behaviour (Hassan, Kunz, Pearson & Mohamed, 2006). The online platform, unlike the traditional market, involves uncertainty because the online vendors are not personally known and the products cannot be touched among others (Naiyi, 2004). Therefore consumers have the fear of conducting transaction online because of the risk of being exploited. Most online consumers consider shopping online as a dangerous activity because unqualified web site infrastructures fail to provide a secure and private transaction environment. The perception of possible risk exposure of online shopping customer is a potential hindrance to the adoption of the service. The risks allied with the online shopping could be, social, financial, functional (performance) or psychological risk (Ariff, et al.., 2014; Arora & Rahul, 2018). Many studies have reported three components of perceived risks that are considered to be particularly important in the adoption of online transactions. These include financial risk, security risk and product risk (Tandon et al.., 2018). 3 More so, a host of the studies on perceived risks towards online shopping focused mainly on the developed economies in Turkey (Arslan, Geçti, & Zengin, 2013), where connectivity and technological infrastructure is high (Effah, 2016). Comparably, studies from a developing country’s perspective are limited and there is a call for research in this regard (Chowdhury, 2003). Hence, a study in a developing nation such as Ghana will generate new insights. Xie (2017) points out that the digital gap of online adoption among countries is at a widening pace, and more studies such as this current investigation will benefit developing countries in taking full advantage of the benefits since attitudes and behaviour differs. There are difference that exist between Ghana (and other developing economies) and those in developed economies in terms of adoption and uptake of technological innovation (Effah, 2016). The difference encompasses both technological development and facilities provision that influence online shopping environment. Thus it is understandable that risk and risk reduction strategies used by these consumers may differ from that observed in other countries. There is the need to identifying these differences so that it can help online companies improve their online marketing strategies. Since online shopping activity is a technologically-oriented form of shopping, it make reasonable sense to expect that some consumers eventually perceive a higher sense of risks with regards to transacting business online. On one hand, some consumers still feel uncomfortable to purchase online due to perceived risk. According to Griffin and Viehland, (2010) suggest that online stores put in measures on their online platforms which helps to minimize perceived risk. Currently in Ghana there are many online shopping sites providing shopping services to hundreds of Ghanaians (Olatunji-Osei, 2010), some of the online shops in Ghana are Jumia, Tisu, Tomato, Olx and zoobashop.com. However, what are the potential risks consumers perceive in relation to online shopping and how can online retailers address 4 these consumer perceived risk dimensions with its underlying issues that has an impact on online shoppers. This study is being conducted in the service industry where technological innovation is very paramount in their operations. 1.1 Problem Statement One of the underlying issues that need to be taken seriously when shopping online is consumer perceived risk. Consequently, for transacting business online to be successful, consumer perceived risk needs to be taken seriously. Some of these consumer perceived risk relates to financial, social, functional (performance) or psychological risk (Arora & Rahul, 2018; Tandon, et al., 2018). In spite of the various efforts by commercial agencies in Ghana to infuse efficiency and speed in commerce through technological innovation, there are still critical factors that make the patronage of online shopping behaviours in Ghana very low (Boateng, Molla, Heeks, & Hinson, 2011). For instance, despite the growth of online shopping a large group of internet users and consumers consider online shopping as risky due to the perception that there are no actual interactions with the product (Dillon, et al., 2014) as a barrier to consumers accepting online shopping. In Ghana, electronic technological innovations for transacting businesses have been met with serious perceived risk challenges in Ghana (Awiagah, et al., 2016). Considering the unique mode of online shopping, perceived risk is usually higher than that of the traditional (face-to- face) shopping (Tamatompol, et al., 2017). Therefore, most customers are not ready to shop online primarily due to risk concerns (Amir & Rizvi, 2017). 5 The major concern is that there is limited research to provide understanding concerning the factors that are responsible to explain the reasons why online shoppers are not accepting something that will ease their shopping activities and save them time (Buzy, 2017). Scholars have therefore called for more, systematic research attention about the relationship between consumer perceive risk dimensions and online shopping behaviour (Buzy, 2017; Padhi, 2017). Reasons for this difference could be cultural since some studies have revealed that culture has an effect on online transactions (Nabareseh, Osakwe, Klímek & Chovancová, 2014). For instance, consumers from individualistic cultures are more likely to shop online than consumers from collectivistic cultures (Kwarteng & Pilík, 2016). It is, therefore, justified to assert that the possible effects of perceived risk on online transactions according to different contexts are issues that merit further investigation (Johnson, 2015). Again, though much studies on consumer perceive risk dimensions and online shopping have been done (Ariff, et al.., 2014; Dillon, et al., 2014), only few that have examined the role of electronic word of mouth in online transaction context (Jalilvand, Esfahani, & Samiei, 2011). Electronic word of mouth is an influential form of sharing information among consumers with regards to online transactions (Jalilvand et al., 2011). These calls for empirical findings to provide effective managerial implications and to contribute to theoretical debate in literature on the role of e-word of mouth between consumers’ perceive risk in online shopping. Finally, available studies on online shopping in Ghana and many other countries in Africa have to pay the needed research attention to consumer perceived risk in technological innovations in order to manage it properly (Kwarteng & Pilík, 2016). Indeed there is the need for more studies that concentrated technological factors affecting online transactions growth in Africa (Kwarteng & Pilík, 2016). Thus, this study seeks to examine online shopping and consumer perceive risk dimensions in Ghana. 6 This requires critical empirical research into preference analysis for online shopping behaviours among online shoppers in Ghana to provide the needed understanding. There is therefore the need to conduct research into the perceived risk issues in online shopping behaviours and acceptance among online shoppers in Ghana. The study will provide empirical understanding into the various issues that impact online shoppers’ use of online shopping behaviours, - a service that can solve the transactional problems of online shoppers. 1.2 Research Objectives The objective of the study was to examine how various dimensions of perceived risk influence online shopping among consumers in Ghana. 1. To determine the relationships between consumer perceived risk constructs and online shopping behaviours among consumers in Ghana. 2. To assess the moderating role of electronic word of mouth between consumer perceived risk and online shopping. 1.3 Research Questions Depending on the objectives of the study these research questions were explored; 1. What relationship exists between consumer perceived risk constructs and online shopping behaviours among consumers in Ghana? 2. How does electronic word of mouth as a moderator influence the relationship between consumer perceived risk and online shopping? 7 1.4 Hypotheses The hypotheses were formulated base on objectives of the study. H1: There will be significant negative association between consumers perceived risk constructs and online shopping behaviours among the consumers in Ghana. H2: Electronic word of mouth will significantly influence the relationship between online shopping behaviours and consumer perceive risk. 1.5 Significance of the Study The study seeks to identify barriers and challenges affecting online shopping behaviours among consumers in an emerging economic like Ghana particularly in the Accra Metropolis. The study therefore serves both practical and theoretical significance. In terms of practical significance, findings from the study provide insight and understanding by revealing online shopping behaviours and the perceived risk factors that influence their willingness and intentions to shop online. The findings inform and educate managers of virtual shops on the importance of consumer perceived risk and the survival of online retail business. Also get policy makers and regulatory authorities to know of the critical areas of concern to online users in order to effect policy direction and enhance proper monitoring. Finally, it will enhance online user satisfaction in this digital age. This provides stakeholders with the needed information to formulate policies and strategies to take advantage of the ever-expanding electronic market across the world. In theoretical terms, the findings from the study makes significant contribution to both theory and literature on the adoption and uptake of online shopping from developing country contexts. The findings thus contribute to broadening the literature and academic discourse surrounding how to increase uptake of online shopping among individuals in low and middle income countries. 8 1.6 Scope of the Study The study investigates consumer perceived risk of Ghanaian consumers towards online shopping and how do online shoppers handle perceived risk in Ghana. The scope of the study therefore encompasses exploring in broader terms online shopping behaviours in Ghana, examining how different elements of risk influence online transactions. For the purposes of the study, online consumers are limited to online shoppers in Accra, the national capital. Among the perceived risk levels examined are included financial, product, privacy, delivery and process and time risks. These levels of risks are examined to identify those that have higher impact on online shopping behaviours among the consumers. Apart from these factors, e-word of mouth is also examined. 1.7 Limitation of the Study The study has some restrictions that should guide interpretation and application of the findings. The study focused on online shoppers in Accra. Those beyond Accra might have some characteristics and features which were not captured in the current study. Therefore, attention needs to be taken in generalising the findings. Again, the study only examined perceived risk. The study did not assess how the participants deal with their perceived risks. The study therefore does not offer understanding into how online shoppers in Ghana manage their perceived risks. 9 1.8 Chapter Disposition The study is organized into six distinct chapters. The chapters are organized as follows: Chapter One: This chapter covers the introductory part of the study, which includes the background to the study, the research problem, research objectives, and the research questions. Chapter Two: This chapter will provide contextual background information of the study. The section provides information, on the service industry, technology penetration in Ghana and an overview of Ghana’s telecommunication service industry with focus on the internet service sector whiles considering the players in online shopping, technological innovation and online shopping in Ghana and challenges of online shopping in Ghana as a whole. Chapter Three: This chapter is devoted to the literature review which involves a discussion of past research work on online shopping. Inclusive in this chapter is a review of studies on consumer perceived risk and its antecedents; and it is important on an online shopping website, how consumers handle risk online shopping and its components, also involves the factors that influence the successful use and relevance of online shopping as well as some underlining constraints and a research framework which entails the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM) were discussed for the establishment of a research model. Theoretical and conceptual issues discussed in past literature are drawn from and built upon. Chapter Four: This chapter takes into account the methodology for the study. The research methodology and discusses issues regarding research design, study site, population of the study, sampling techniques and procedure for data collection. The chapter also provides the procedure for data collection and the ethical principles that were adhered to. 10 Chapter Five: This chapter provides the statistical analysis and results of the study based on the hypotheses tested. The chapter further presents discussions of results obtained in relation to relevant literature. Finally chapter six provides the conclusion of the study and discusses issues such as summary of findings, conclusions and recommendations for increasing cashless payment acceptance among the online shoppers. 11 CHAPTER TWO CONTEXT OF STUDY 2.0 Introduction This section seeks to provide contextual background information of the study. The section provide information, on the service sector, technology penetration in Ghana and an overview of Ghana’s telecommunication industry with focus on the internet service while considering the players in online shopping, technological innovation and online shopping in Ghana and challenges of online shopping in Ghana as a whole. This would help the reader position the study in a particular context that would aid understanding of the study area. 2.1 The service sector in Ghana In Ghana, the telecommunication industry relates to services that involves the exchange of information over significant distances by electronic means (Effah, 2016; Lee & Barnes, 2016). The concept has been described to include service related activities such as voice transmissions, data services, text, sound and video which may be based on a single technology or complicated with a combination of technologies such as communication satellites and the internet (International Telecommunication Union, 2010). According to the National Communication Authority (2014), there are 6 main cellular service operators with about 165 internet service providers (ISP) in Ghana. In the 1990`s, the service sector of Ghana contributed little to GDP at a steady rate. At that time, the agricultural sector was known for its dominance followed by the service sector and the industry. With time, the service sector has seen incredible performance, notably the remarkable growth in 2010 (IFPRI, 2012). The International Monetary Fund (2014) recorded an overall economic growth rate of 5.4 % in 2013. From this percentage, the services sector recorded a 0.7% growth with 12.7% from information and communication services including 12 mobile telecommunications and other internet based services (ISSER, 2013). This growth is followed by the agricultural sector and manufacturing. Recently, the state of the Ghanaian domestic economy is seen to be also revolving around services sector; which accounts for fifty percent (50%) of gross domestic product and employs twenty eight (28%) of the work force (GSS, 2014). Gradually the county is acknowledging the need to invest in a healthy digital supported service economy and leads among its neighbours in West Africa (Ghana National Commission for UNESCO, 2007). In Africa, Ghana was one of the first countries to achieve connection to the World Wide Web. As at 2014, the country recorded 5,171,993 internet users out of 297,885,898 for Africa (Miniwatts Marketing Group, 2014). For this reason among others, there has been a spark in creation and growth of businesses across the economy with services like telecommunications as no exception. A telecommunications industry performance review by Frempong (2007) has indicated that, there have been responses to global changes including substantive reform of telecommunication markets. Some other reports have recognized telecommunications as a significant area in the Ghanaian service sector (PWC, 2014; KPMG, 2013). The Ghanaian service sector is currently doing very well as compared to the agricultural sector which used to be the backbone of the Ghanaian economy. In recent times, the service sector seems to be the key driver of several economies, even more so with technological growth and globalization. Ghana is currently considered as one of the fastest growing countries in terms of information technology and social media usage in West Africa (Buami, 2013). The sudden rise in information technology and social media usage has been found to be more among students in Ghana right from primary school through to the tertiary levels peaking at the universities. The purpose of usage of social media has changed significantly among various groups depending 13 on their needs. For example, social media use among journalists in Ghana has been found to change from a portal for friendly chat to a platform for disseminating news (Deo-Silas, 2013), the usage for teen students have also changed from a portal for chatting with friends to sourcing for leaks, news, studies and making new friends (Buami, 2013). The mostly used social media include WhatsApp, Instagram, Twitter, Facebook, Tango, My Space, LinkedIn etc. usually through internet connectivity on phones, ipads, tablets and or laptop computers. These are making people get used to the electronic platforms in the various aspects of their lives which will encourage electronic commerce and transaction with few years to come. In addition, the current internet usage rate as reported by Internet World Statistics report (2017) indicates that as at June 2017, the internet usage rate in Ghana was 34.7% out of a population of over 27million; whiles Africa the internet usage rate was 31.2% out of 388,104,452 internet users. The overall internet penetration rate in Ghana increased from forty percent to forty point seven percent as at the end of August, 2013(Kwarteng & Pilik, 2016). These are as a result of the proliferation of smart phones and other electronic devices, this usage rate of the internet is a vital factor in predicting the triumph of online shopping, the opportunities it presents. However enough research has not been conducted with regards to what Ghanaian consumers do mostly with the internet; if access to the internet has caused consumers to purchase goods and services online. 2.2 Players of some Online Shops in Ghana This section provides some background to some online shops in Ghana. According to Olatunji-Osei (2010) reveals that in Ghana online shopping companies have increase such online companies provides online services to hundreds of Ghanaians. Some of the online companies in Ghana includes Olx, Tonaton, Jumia, Kikuu, but Tisu and Zoobashop were 14 chosen for this study because they were the first online shops the researcher contacted during the pilot study. Tisu.com.gh Tisu is an online shop that deals in goods and services. It has offices in 13 countries around the world. Tisu operate in Europe, Asia and Africa. Tisu also provides its customers an exciting means of getting discounts. Their website is updated every day with unbeatable prices and valuable local experiences on entertainment, electronics, spas, restaurants, and travel. Tisu.com features a single offer to drive hundreds and thousands of customers to a business on a daily basis. It uses the creativity and experience of its business developers to help design an offer tailored to meet the needs of businesses. Businesses that advertise on the website are not charged anything. However, a commission is taken on every deal they are able to sell. This makes Tisu.com risk free. When a business is featured on a particular day, thousands of their subscribers receive deals in their inboxes, read about the business, and buy the deal with a few easy clicks. Tisu.com uses a principle of collective buying which requires a minimum number of buyers or the deal is off. The minimum is achieved when their savvy customers use social media tools like Facebook, email, twitter, and SMS to spread information about deals. As a result featured businesses gain exposure and trend. Checks are sent to the featured businesses a day after the deals are closed and they are provided with easy to use tools to help them track customers as they redeem their deals. Zoobashop.com Is an online retail store in Ghana which offers the best for their cherished customers? Products on sale are categorized into computers & electronics; mobile phones; home decor 15 and appliances; fashion; food and drinks; baby, kids and toys; books, movies and games; and health and beauty. They try to make the search for products free from hassle on their website. One will just have to type the product they are searching for into a search box and various kinds of that product along with varying prices will be displayed in the window. To provide customers a wide range of products to choose from, Zoobashop.com has renowned brands including Apple, Blackberry, Colgate, Samsung, and Beko. Furthermore, Zoobashop.com also allows various payment options which include debit and credit cards (Visa and MasterCard branded ATM cards), cash on delivery among others. They also deliver to the doorstep of customers by providing various delivery options. Customers can browse the extensive range of products in their online store, and when they find the products they are interested in, they go through the following simple steps: Click Add to Cart so that they can put the items they want in their shopping cart. The orders are delivered to the homes or offices of customers. Businesses whose products are listed on Zoobashop.com grow their customer base instantly because they get access to thousands of customers who visit the website. Marketing of items are done through social and traditional media platforms such as Facebook, WhatsApp, radio, and newspaper among others. There are no recurring fees, which means once an agreement is signed, businesses do not incur additional charges for listing new products or any transactional fees. For a business to be listed on Zoobashop.com, it must be legally registered in Ghana and must be able to guarantee stable and reliable supply of items. 16 2.3 Technological Innovation and Online shopping behaviours In the current dispensation of advance technological innovations, information has become key resource that individuals can leverage for their personal, academic and career advancement. Technological advancement has provided several avenues where individuals can search information from, especially for their economic aspects of their lives. The internet together with its applications (the most well-known being the world wide web) is the infrastructure that brings together people, in different places and time zones, with multimedia tools for information, communication, data and knowledge management in order to enlarge the range of human capabilities (Duranti, 2010). It aims to help in international development by bridging the digital divide and providing equitable access to technologies. The internet is mostly used by people to get easy access to their information requirements through the use of handy electronic devices such as tablets laptops, smart phones, and other communication devices. In this information age, most researchers have made the internet a place where they obtain their information needs There has been an increase in the attempt to find speedy and efficient ways for financial transactions across the world which has culminated into various technological innovations with cashless payment system or electronic payment being one of the key of such initiatives (Zendehdel & Paim, 2012). Online shopping behaviours is a technological innovation that enable business transactions to be carried out without using the physical presence of the buyer. Buying and purchase transactions are instead carried out using technological platforms such as credit card or electronic transfer of funds (Hsieh & Tsao, 2014). Online shopping behaviours form part of a broader technological innovation referred to as electronic payment (E-payment for short). In the current study, cashless payment system and e-payment are used interchangeably. 17 Electronic commerce in general helps both parties in business transactions in terms of solving with many of the challenges that characterises day-to-day purchases. E-commerce, apart from serving as a source of needed information for online shoppers, it also offers them new markets. Online shopping in particular help to make transactions very efficient by eliminating some most of the challenges associated with traditional shopping such as looking running around town moving from shop to shop for products (Khanna, 2017). Among online shoppers for instance, online shopping saves them time, which they can leverage for other economic activities (Arora & Rahul, 2018; Dillon et al., 2014). Electronic commerce has come at a period time that electronic payment is increasing across the globe. On daily basis, several people are gaining access to electronic platforms of payment due to advance in technology, especially with the introduction of the internet. In current times, Africa is seen as the continent with increasing growth of markets for internet services with proliferation of mobile telecommunication companies (Yang, Pang, Liu, Yen & Tarn, 2015). In Ghana for example, internet and electronic access currently is on a steady rise with an estimated eight million people using one electronic technological innovation or the other (Awiagah, Kang and Lim, 2016). Banks and telecommunication companies have been at the forefront of providing online shopping behaviours in the Ghanaian market. Because more and more people and business organizations are using the electronic payment platforms, there is lot of potentials for shoppers who utilize online shopping behaviours. The rise in the use of electronic payment platforms is helping local businesses by; increasing visibility of businesses, forging closer ties between businesses and customers and trading partners thereby deepening business relationships ( Slade et al., 2015; Yang, et al.., 2015). Individuals who appreciate the utility of cashless or electronic payment systems and make good use of it increase their chances of achieving more strategic benefits in terms profits (Khanna, 2017). 18 2.4 Challenges with Online Shopping Acceptance in Ghana In developed countries, online shoppers benefit greatly from the use of the internet through E-payment. Again, E-payment is being used as an auxiliary means of marketing all kinds of products to consumers by shortening the supply chain (Strz, 2011). Going by the way online shopping has revolutionized business transactions, mainly in developed economies, the argument can be made that improvement in the expansion and access to the e-payment platforms also has the potential to improve commercial activities in developing countries (Awiagah, et al., 2016; Slade et al., 2015; Yang et al., 2015). These potentials notwithstanding, across all developing economies over, firms engaged in electronic payment platforms are striving hard to ensure high uptake among the general population (Yang, Pang, Liu, Yen & Tarn, 2015). However, online shopping behaviours in sub-Saharan African countries including is faced with several challenges and limitations. These challenges go beyond just access. The major challenge has to do with a general lack of understanding and the wrong perceptions people hold about electronic commerce platforms (Awiagah et al., 2016). Africa still remains one of the continent with limited penetration of e-commerce and therefore a lot remains to be done if Africa is to parallel the developed economies in terms of online shopping penetration, access and use (Awiagah et al., 2016). The problem with acceptance of online shopping in Africa lies with those with low levels of education in general, which exposes most people to perceived risks associated with online shopping (Oliveira, Thomas, Baptista & Campos, 2016). In Ghana for instance, several attempts by various agencies to get individuals in general and online shoppers in particular to utilize e-commerce platforms have been met with severe challenges as most of them are unwilling to do so (Awiagah, et al., 2016). The situation in Ghana is currently characterized by low internet connectivity, high levels of electronic fraud, 19 high levels of illiteracy and an underdeveloped and volatile agribusiness industry. This creates a situation militate against the effort to accelerate the growth of online shopping in Ghana (Awiagah et al., 2016; Boateng, et al., 2011). Adoption of online shopping among online shoppers in Ghana is problematic in terms of general preference and uptake (Awiagah, et al., 2016). The study therefore examines preference for online shopping behaviours among online shoppers in Ghana to provide further insight. 20 CHAPTER THREE LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK 3.0 Introduction This chapter situates the study within relevant literature concerning technological adoption in general and adoption of online shopping behaviours in particular. In the literature review chapter, the theoretical basis of the study is first provided. This provides theoretical argument of how to promote technological adoption in general. After that, a conceptual review is discussed. The chapter then proceeds with a review of related studies takes a critical look at the relevant studies in the light of this study’s objectives. Then a summary of the review of related studies is provided. 3.1 Theoretical Framework Theoretical framework plays significant roles in research as means of guiding the entire research process. The fundamental unit of theoretical frameworks is a theory or a model (Cherry, 2016). A theory is defined as a system of ideas that are organized to explain a phenomenon (Awa et al., 2015). Theories are answers to questions pertaining to why people behave the way they do in a particular way. In research, theories serve as a guide to developing research questions, interpreting data and also serves as framework for discussing findings. Within the context of research, theories shape how ideas and concepts are organized together to achieve the overarching aim or purpose of a study (Yang, Liu, Li & Yu, 2015). In this sense, theories are formulated to help in explaining, predicting and understanding, or even challenging assumptions in pre-existing knowledge in order to broaden the boundaries of knowledge (Yang et al., 2015). Theoretical framework is therefore created by synthesising 21 the arguments and assumptions of more than one theory or model to provide a broader context of guiding research process (Yang, et al., 2015). A theoretical framework therefore acts as a structure that hold or support to anchor the entire research process (Awa, et al., 2015). Research on electronic commerce uptake broadly falls within the field of technological adoption on one hand and diffusion of technological innovation on the other hand (Awa, et al.., 2015). Therefore, technological adoption and diffusion theories and models constitute appropriate theoretical frameworks for explaining adoption of online shopping behaviours within the context of the current study. Different theories and models have been proposed to explain adoption and diffusion of technological innovation. These include; Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Within the context of the current study, the TPB and the TAM are used as the theoretical framework for explaining the online shopping behaviours adopting. 3.1.0 Technology Acceptance Model Technology acceptance model (TAM) was developed by Davis et al. (1989) as a model for explaining how individuals come to accept a new technology. The fundamental assumption of TAM is that adoption of technological innovation is mainly a behavioural issue (Davis, 1989). Therefore, there are some psychological and social factors influence people’s decision to adopt a new technological innovation (Misango, Njeru & Kithae, 2016). These factors include different elements perceived risks that consumers and potential consumers associate with a new technological innovation. TAM proposes for instance that both perceived risks 22 influence the perceived ease of use and the two influence the individual’s attitude toward using the technology (Misango et al., 2016). Perceived risk is conceptualised as the extent to which individuals find the new technology very risky. One individuals find a technological innovation risky, it affects their perceived use. Perceived usefulness on the other hand is conceptualised as the extent to which individuals believe that using the new technology would improve their lives compared to not using it (Davis, 1989). Perceived risk is defined as the extent to which individuals believe the new technology would pose risk to their lives (Davis et al, 1989). Facilitating conditions are the necessary conditions available for individuals to conveniently use the new technology. Several studies have applied the TAM to test individuals’ use of different technological innovations including online shopping intentions (Misango et al., 2016), website use (Awa, Ojiabo &Emecheta, 2015), electronic collaboration (Rosaci &Sarnè, 2014) and e-mail (Bhattacharya & Mishra, 2015; Gefen & Straub, 1997). The results from these studies broadly show that the TAM adequately explains people’s intentions to use and their actual use of technological innovations. The TAM is therefore applied to the current study in arguing that adoption of online shopping behaviours among online shoppers in Ghana is influenced by the different elements of perceived risk, which all constitutes the kind of attitudes they hold about cashless payment system. 3.1.1 Theory of Planned Behaviour The Theory of Planned Behaviour (TPB) on the other hand was developed by Ajzen (1991) to explain people’s intention in engaging in a new behaviour in general, not just those that are related to technological adoption per se. However, because adopting online shopping constitutes a new behaviour, the TPB is fit to be used as a framework. The TPB was 23 developed from the theory of reasoned action (TRA) which was meant to explain intentions behind engaging in a new behaviour rather than the behaviour itself (Ajzen, 1991). However, the TRA was found to be limited in predicting actual behaviour and so the TPB was develop to fill the gaps. The fundamental assumption of the TPB is that for individuals to engage in a new behaviour, they must first develop intentions to engage in the behaviour. The theory proposes further that intention in itself is influenced by several psychosocial factors (Ajzen, 1991). These factors mainly include risks associated with the behaviour, perceived behavioural control and subjective norms. Perceived behavioural control is the extent to which individuals believe that they can exercise control over the new behaviour they intend to engage in. Social norms is the pressure that individuals feel within their social environment to engage in the behaviour. Social norms also involves the things individuals hear about the technological innovation. In applying Theory of Planned Behaviour to the current study, for online shoppers in Ghana to use online shopping behaviours, their intentions to use online shopping have to be there before the adoptions and use of actual behaviour (i.e. shopping for products online) of using it will follow. The intentions of the consumers to use online shopping, will be affected by the different kinds of risks that they associate with online shopping. Therefore, in order to understand online shopping behaviours among the online shoppers in Ghana, it is imperative to assess all these elements of risks they associate with online shopping. It is also imperative to assess how the things individuals hear about online shopping influence their online shopping behaviour. In this study, electronic word of mouth is examined within the context of what individuals hear about online shopping. 24 3.1.2 Summary of Theoretical Framework Putting the two theories (technological acceptance model and theory of planned behaviour) together, factors that influence online shopping behaviours that were explored in the study were attitude towards online shopping behaviours, different elements of perceived risks associated with online shopping and electronic word of mouth. 3.1 Conceptual Review The section discusses the main concepts used in the current study. Two main concepts are discussed here. These are consumer perceived risk and online shopping behaviours. Consumer perceived risk is discussed first and is situated within the broader context of the concept of risk in consumer behaviour research. Online shopping behaviour is discussed next and is also situated within the broader context of adoption of technology behaviour research. 3.2.0 The Concept of Consumer Perceived Risk The concept of risk has featured strongly in consumer behaviour research from different disciplinary perspectives. Risk in consumer behaviour has been researched from mainly from economics, psychology and marketing disciplinary perspectives. Each of these perspectives have different approach to studying consumer risk, albeit psychology and marketing perspectives align. From the economic perspective, risk has been defined as the variations associated with the distribution of possible outcomes in economic decisions of consumers and the likelihood of positive outcome occurring (Ahmed et al. 2017; Arora & Rahul, 2018). Other researchers have also defined risk from economic perspective as the chances associated with a particular event occurring and the impact it would have on the consumer (Bhattacharya & Mishra, 2015) 25 In economic sense, every decision a consumer makes is associated with some element of risks (Boateng et al., 2011; Burt & Sparks, 2003). This is because every consumer decision has an opportunity cost and therefore the risk associated with the decision is whether or not the outcome of that decision will be better than the alternative forgone (Domeher, Frimpong & Appiah, 2014). In this sense, economists’ way of studying consumer risk has been approached from the objective point of view by calculating the economic cost and gains of consumer decision (Doolin, Dillon, Thompson, & Corner, (2005). Economists therefore conceptualises consumers as rational human beings whose economic decisions are influenced by a dispassionate cost-benefit analysis (Dillon, Buchanan& Al-Otaibi, 2014). The economic way of studying consumer risk has been criticized heavily, especially by social psychologists who study human behaviour in general and consumer behaviour in particular. The fundamental assumption by economists that consumers always make rational cost-benefit analysis in economic sense when making economic decision has been heavily criticized by social psychologists (Buzy, 2017; Bhattacharya & Mishra, 2015). Their fundamental arguments are that consumers, as human beings, are complex beings who are influenced by complex set of factors that go beyond economics. One critical factor that is argued to influence economic decisions of consumers is their subjective emotional experiences which shape their thought process and how they make economic decisions (Awiagah, Kang& Lim, 2016). The study of consumer risk is therefore encourage to be also looked at from the perception level in order to understand the subjective experience of fear that influence consumers’ decision. The psychology and the marketing perspectives therefore align in conceptualising consumer risk. Both disciplines examine perceived risk in order to deeply understand how different individual consumers orient themselves with their object of economic decision making (Ariff, Sylvester, Zakuan, Ismail& Ali, 2014). Consumer perceived risk therefore is the social 26 psychological perspective that assesses how consumers perceive uncertainties and the fears associated with making economic decisions (Arora & Rahul, 2018). Within the context of the current study, the decision to engage in online shopping is influenced by elements of uncertainties and fears that consumers associate not only with electronic commerce in its broader sense, but also with online financial transactions in its narrower sense. Different elements of consumer perceived risks have been proposed, each addressing specific domains of fear of uncertainties associated with engaging in a new behaviour. Research on the multidimensional measurement of consumer risk was spearheaded by Cox (1967) and since then, other researchers have developed different elements of perceived risk (Boateng, Molla Heeks & Hinson, 2011). These dimensions include; perceived financial risk; fear of potential loss of money or additional charges or threats to finances as a result of engaging in a behaviour (Arora & Rahul, 2018), perceived physical risk; fear of potential harm to health (Bhattacharya & Mishra, 2015), perceived social risk; fear of loss of one’s social group (Domeher, Frimpong & Appiah, 2014), perceived privacy risk; fear of having unrestricted access to private accounts (Paluch & Wünderlich, 2016), delivery risk; fear of not receiving the products paid for online (Arora & Rahul, 2018), time risk; fear of not getting the order on time (Paluch & Wünderlich, 2016) etc. The perception of possible risk exposure of an online shopping customer is a potential hindrance to the adoption of the service. The PR associated with the online shopping transactions could be financial, social, functional (performance risk) or psychological risk (Arslan et al., 2013; de Kerviler et al., 2016; Kim, Jang, & Yang, 2016; Paluch&Wünderlich, 2016). Financial risk is the possibility of a client losing money due to unauthorized persons having access to the client’s credit card account. Social risk is the influence or perception of society regarding the service. For instance, the perception that the online shopping is for the working-class people and highly educated in society since an individual may feel down. 27 Functional risk is the inability of the service or the product to perform to the satisfaction of the customer; for example, fear of failure to perform or function the way it was promised. 3.2.1 Online Shopping Behaviours Online shopping behaviour is situated within the broader context of electronic commerce (e- commerce) and electronic payment (e-payment) adoption research. Online shopping is the acts of engaging in buying and selling of mainly products (but sometimes services) electronically (Paluch & Wünderlich, 2016). Online shopping behaviours therefore encompass all the behaviours associated with engaging in electronic commerce and transaction (Bhattacharya & Mishra, 2015). These behaviours include scanning for information about products, visiting online stores, placing order for products, putting personal products online for sale, paying for products online, scheduling delivery schedules online etc ( Arslan et al., 2013; de Kerviler et al., 2016; Kim, Jang, & Yang, 2016; Paluch & Wünderlich, 2016). Generally, some individuals are of the view that product information that are usually provided on online don’t constitute enough information for making sound purchasing decision. Many times, some online shoppers report of being disappointed when the product information does not meet their expectation (Paluch & Wünderlich, 2016). Balasubramanian, and Bronnenberg (1997) argued that certain characteristics of products and services are well suited to online shopping. For example, compared to experiential goods, whose value can be truly determined by their consumption ( a bottle of wine or health care), search goods are more suitable for online shopping because their value can be evaluated from externally provided information prior to purchase (paper or air plane tickets). 28 Online shopping is far advanced in high income countries. In low and middle income countries however, online shopping is struggling with both acceptance and uptake. Apart from the limited infrastructures that promote online business in low income countries (e.g. high speed internet connectivity, proper addressing systems, electricity etc.), there is a general fear among the general public associated with online commerce and transaction (Bhattacharya & Mishra, 2015). In Ghana for instance, majority of individuals still prefer traditional shopping option where they have access to the tangibility of the goods or products that they purchase (Domeher, Frimpong & Appiah, 2014). In addition, intangible or service-related products are more suitable for online shopping than tangible or physical products. Thus, the suitability of online shopping pertains to the perceived risk. In other words, the products that individuals feel are less risky when purchasing online is highly suitable to online marketing (Domeher, Frimpong & Appiah, 2014). Biswas and Biswas (2004) found that consumers felt less risk when purchasing products online that have high digital attributes (e.g. a music CD) than they did in a brick- and-mortar store. Dai, Forsythe, and Kwon (2014) also found that consumers felt more security risks when purchasing non-digital products, such as apparel, compared to digital products. Taken together, these empirical findings indicate that certain product characteristics are strongly associated with perceived risks. 3.3 Review of related Studies The review of related empirical studies are provided in this section. This mainly include studies that have gathered primary data to research the mechanisms that explain individuals’ adoption of new technologies. Because there are limited studies among online shoppers in general, the studies reviewed encompasses those that have examined adoption of electronic 29 platforms among different groups of people including bank customers. Due to the fact that electronic payments systems is a technological innovation, majority of the studies have also applied TAM to study of adoption of electronic payment systems in different context. These studies report that TAM is a good model for predicting the acceptance of technological innovations in the financial market. The review of related studies is organized around two main themes; i) perceived risk factors influence technological adoption (ii) the influence of e- word of mouth on consumer perceive risk in online shopping. 3.3.0 Perceived risk factors and adoption of online shopping behaviours Even though there are limited studies among online shoppers, the few of them provide some insight into the factors that may influence online shoppers to adopt electronic payment systems. For example a company in the Eastern region of Ghana decided to venture into E- payment because of certain factors such as cost reduction and its convenience of use. The manager of the company testified that indeed E-payment has enabled them reach a wider market with much ease whilst reducing costs related to marketing (Boadi, et al., 2007). In another related study conducted by Kinyangi (2014), the socio-economic features of individuals also has an impact on their willingness to adopt a new innovation. These features include their age, educational status, income, family size and religion amongst others. A study carried out in Japan showed that old aged people were not willing to use new electronic platforms because they saw no problems with their current systems based on their experience. Other factors include availability of resources, available markets, affordability of the new technology, low level of risks associated with the technology as well as the benefits they would derive from its usage. Another reason that serves as basis for online shoppers to opt for a particular technology is their perception about it (Kinyangi, 2014). 30 Some research studies have also applied the TPB to investigate engagement in using electronic banking. Findings from these studies broadly show that using electronic banking is influenced by several factors. For instance, Yaokumah et al., (2016) investigated the extent of development and factors that influence the use and satisfaction of electronic payment services in Ghana. They used a sample of 588 participants who were conveniently sampled from the customer base of nineteen banks in Ghana. They found that perception of security of e- payment, cost of technology and service reliability significantly influenced customers’ willingness to use e-payment services. Interestingly however, literacy of information technology did not account for significant variance in the use of e-payment services. Even though the banks have deployed different e-payment services, substantial number of the customers (42%) indicated that they still preferred the traditional banking methods in addition to e-payments with 4.8% indicating that they prefer only traditional banking methods. In similar studies that were carried out in Ghana assessing challenges for the adoption of e- zwich in various parts of Ghana, Issahaku (2012) and Antwi, Hamza and Bavoh (2015) have reported different factors that pose challenge to e-zwich adoption in Ghana. For instance, using a sample of 50 users and 30 non-users of e-zwich, Issahaku (2012) found that factors such as link failure, frequent breakdown of machines, slow process of service delivery, long queues and inaccessibility of the point of sale devices before and after banking hours pose severe challenge to e-zwich adoption. Antwi et al., (2015) also reported same findings indicating that frequent link failures, long queues in banking halls and limited point-of-sale devices negatively affect e-zwich adoption in Ghana. Boateng, Tetteh and Boateng (2015) also researched managerial and usage challenges associated with e-zwich payment system in Ghana using 102 users and 5 managers of commercial. Their main findings were that held the view that the complex processes involved in conducting payments posed a challenge to e-zwich adoption. Therefore, lack of knowledge 31 and skills in basic computing; lack of trust in non-cash payments and inadequate marketing (advertisement) campaigns are also challenge to e-zwich adoption. The finding of concerning knowledge and skills in basic computing skills hampering e-zwich adoption is contrary to the finding by Yaokumah et al., (2016) that literacy in information technology does not account for significant variance in the use of e-payment services. Agwu, Atuma, Ikpefan and Iyoha (2014) have also reported among bank customers in Nigeria that there are several perceived risk factors that act as barriers preventing the customers from using mobile banking services. They found among the customers that majority of them still preferred the traditional banking services compared to the technologically-oriented mobile banking services. Also, a study by Al-Smadi (2012) that tested the TPB in predicting the use of internet banking reported that perceived behavioural control and attitude significantly influenced customers’ intention to use internet banking. This suggests that attitude towards the behavioural was very important in deciding whether to engage in the behaviour. These studies however do not tell the full story. Other studies have also examined attitudes towards a new technology and how they predict intention and actual use of the technology. For example, a study by Martins, Oliveira and Popovic (2014) among the general public found that, social influence was very powerful in explaining the use of electronic payments. They therefore reported social influence to be a stronger significant predictor of intentions and adoption of internet banking among customers. Godoe and Johansen (2012) also conducted a study among Norwegian sample and found that optimism had significant positive effect on the use of innovative financial technologies. 32 3.3.1 Electronic word of mouth This section presents synthesis of findings from difference studies that have examined differences in adoption, intention to use and actual use of technological innovations in general. This is imperative to provide understanding into how electronic word of mouth influence consumer perceive risk in online shopping. Electronic word of mouth is the sharing of positive or negative information between consumers that concerns a service or product (Gupta & Harris, 2010). In this modern times, the advent of technology innovations has led to increase in information sharing among consumers through online platforms such WhatsApp, Facebook and Twitter to share information about products and services they purchase (Gupta & Harris, 2010), with regards to what they purchase from firms (Lee, Shi, Cheung, Lim & Sia, 2011). Again electronic word of mouth comes in two forms. Positive and negative word of mouth, with the positive e- word of mouth is when consumers are satisfied gives good testimonies, which helps to promote the operations of the firm (Royo-Vela & Casamassima, 2011), while customers who are not satisfied with the service gives bad report which affect the services of the firm in question through the internet (Yang, 2017). This implies that the more consumers hear good testimonies, recommendations from friends and close relatives, it reduces the perceive risk associated with technology innovation like online shopping. Electronic word of mouth is alleged to be influential in adopting new technological innovations like online shopping, for instance e-word of mouth is powerful in terms of information exchange through the internet with no geographical barrier, and also serves as a link between firms (Hussain et al., 2017). This is done by the effort of the individuals in question, by posting or texting when making purchase decisions based on the information, ideas, thoughts and recommendations from others (Gupta & Harris, 2010). In such services like online shopping, where perceive risk is high (Paluch & Wünderlich, 2016), e-word of mouth have proven to be a strong marketing 33 strategy such that consumers consult their friends when making purchase online (Taghizadeh et al., 2013). Furthermore, most customers have confidence in friends and relatives more than firm’s information (Ng et al., 2011). This shows the more information customers get from people they valve in society with regards to online shopping the risk they perceive reduces. Over the years research has proven that word of mouth is considered more credible than advisement (Cheung & Thadari 2012) and also a strong source of information about service and product (Lee & Youn, 2009). Electronic word of mouth has influence on consumers in terms of transacting business online for instance consumers usually believe friends and family members more than the company they purchase from (Nie to et al., 2014), also making it a more reliable source of information which consumers depend on (Lee & Youn 2009), when consumers want to make purchase they search for information that is more credible (Wangenheim & Bayon, 2004). Also e- word of mouth gives customers the opportunity to exchange information they have with their friends about product or service of a firm which helps to determine what and how they purchase (Jalilvand & Samiei), which firms have no control of the kind of information customers share ( Yang, 2017). More so friends, family members who are online shoppers can influence others to shop online, this indicate that consumer’s attitudes are influence by what people say or do. 3.3. Summary of literature review There have been interesting findings from the studies reviewed above but a close scrutiny of the literature shows that the issue is far from over and that there are issues that need further clarification with empirical investigations. Even though TPB and TAM have explained adoption of financial innovation well, there are inconsistencies in the findings that need to be reconciled with further investigations. 34 For instance, whiles some researchers find perceived risks as the most significant predictor of financial innovation (Cudjoe et al., 2015; Al-Smadi, 2012), others found that performance expectancy and social influence are the strongest predictors of acceptance of innovation (Domeher et al., 2014; Martin et al., 2014). Other studies also found facilitating conditions as the most significant barriers to adoption of financial innovation (Agwu et al., 2014). But in most situations, these two theories have been tested individually which do not give a clearer picture when they are tested together. The essence of the studies reviewed is that, institutions engaged in promoting cashless payment system adoption require context-relevant insight in order to make decisions concerning the best way to promote adoption. However, the literature is full of inconsistencies in the findings and so there is no clear picture for firms to use. This call for a further investigation in order to reconcile the inconsistent findings to inform banks’ effort of promoting cashless payment system adoption. The study contributes to this agenda to promote online shopping behaviours among online shoppers in Ghana. 3.2 Proposed Model Based on the studies reviewed, this section discusses proposed conceptual model predicting online shopping behaviours in Ghana. The proposed model is represented by Figure 3.0. As shown on Figure 3.0, the study proposes five elements of consumer perceived risks (i.e. financial, privacy, product, delivery and process and time loss) significantly predicts online shopping behaviours in Ghana. The effect of consumer perceived risk dimensions on online shopping behaviour is proposed to be moderated by electronic word of mouth. 35 E- Word of mouth  Online reviews  Recommendation from actual customers  Post/comments from online chat sites Consumer perceived risk Privacy risk Online Shopping Behaviour Product risk  Scanning for information about Delivery risk products  Paying for products online Process and time risk  Scheduling delivery schedules online Financial risk Figure 3.0: Hypothesized Model of Consumer Perceived Risk and Online Shopping Behaviour 36 CHAPTER FOUR RESEARCH METHODOLOGY 4.0 Introduction This chapter contains the detail description of methodological processes involved in gathering the data for the study. The sections captured here; research design and approach, research setting, selection of participants (population, sample and sampling technique), measures for data collection, ethical consideration, data collection procedures and data analysis. This chapter begins with a description of the philosophical foundations and provide reasons for the philosophical stance chosen for this study. 4.1 Philosophical Foundations A typical research need to articulate a philosophy that provide the framework for conducting a research study. These philosophies are assumptions about gaining knowledge. The assumptions provide a guideline for conducting research (Creswell, 2011). To begin with, a framework is needed how research philosophy fits into the design of a study. According to Crotty (1998) indicate that there are four major elements in the design of a study namely constructivism: positivist participatory and pragmatism. Positivist Approach: This approach is often associated with quantitative methods. Information is obtained based on cause-and-effect thinking, reductionism, which require scholars to narrow and focus on select variables to interrelate. Information is also acquired by observations and measures of variables, in other instance it requires the testing of theories that are continually developed (Slife & Williams, 1995). 37 Constructivism: this approach is aligned with qualitative method where information is derived from the subjective views of respondents. The understanding of respondents on issues is largely shaped from social interactions and also from their own personal experiences. Experiences observed from others also form part of the constructivism. Participatory: this approach is linked with qualitative than quantitative method, through research inquiry. This approach is influenced by the political need to improve society and its occupants. In this regard, participatory worldview seeks collaboration with individuals or groups experiencing a given social problem for solutions. The motive of this world view is to provide guide for making society and its inhabitants better. Pragmatism: this approach focus more on stressing on the importance of asking questions instead of method used this brings on board a lot of methods and the best one that fit in the practical context is used. In this research the chosen philosophy was the positivist approach and the philosophy of positivism as the collection of data is prepared to search regularities, patterns and causal relationships to create generalizations about them. In other to ensure that there is no biasness in the study, the positivist approach ensures as much as possible that the data collected is neutral and not influenced by the researcher's feelings (Saunders et al., 2009) 4.1 Research Design and Approach In this study, quantitative research approach was used. A research design and approach is defined as the overall strategy that is chosen to integrate different parts of a study in a coherent manner to address a research question (Bryman & Bell, 2015). Creswell (2014) also defined a research design as the specific way and process or techniques that a researcher 38 follows in gathering and analysing data. There are two main categories of research approaches, which are quantitative and qualitative research approaches (Creswell & Creswell, 2017). Quantitative research is the kind of research approach that is usually undertaken with data collected in the form of numbers from large sample of participants (Bryman & Bell, 2015). Qualitative research on the other hand is undertaken with data collected in the form of narratives or observation using small sample of participants (Bryman & Bell, 2015; Creswell & Creswell, 2017). There are instances where, both quantitative and qualitative approaches are used, as such; research design is referred to as mixed-method. The decision as to which research approach to adopt, among other things is influenced by several factors including the research question and the extent of research conducted on the subject matter within the context of the study (Creswell & Creswell, 2017). The quantitative approach was used because the study examines relationships between consumer perceived risk dimensions and online shopping behaviours among a large sample of online shoppers in Accra which needs to be measured numerically. Specifically, the cross- sectional survey design was used to gather the data. Cross-sectional survey is defined as a data collection method where a researcher collects data from a representative cross section of the population of interest in order to understand the situation (Creswell & Creswell, 2017). The cross-sectional survey design was chosen because it is appropriate for descriptive, explanatory and exploratory purposes and are mostly used in studies that have the individual as the unit of analysis (Creswell, 2014). The adoption of the cross-sectional quantitative survey design allowed for data to be collected on a large number of online shoppers who differ on one key characteristic at one specific point in time so that the results can be generalized (Creswell & Creswell, 2017). 39 4.2 Research Setting The study was carried out among online shopping selected within a particular district. Specifically, the data was collected from the Accra Metropolis which is one of the most popular districts in Greater Accra region. Research settings can be organizations or institutions, community, district, region or a country based on what the focus of the study is. The study was carried out among online shopping selected within a particular district. Specifically, the data was collected from the Accra Metropolis which is one of the most popular districts in Greater Accra region. The Accra Metropolitan Assembly (AMA) is the administrative and political authority for the city of Accra. The AMA is one of the biggest and the busiest metropolis in the capital, where there is heavy presence of online stores and online shopping activities compared to other parts of the country. Due to this, AMA serves as the best place for the study of online shopping behaviours in Ghana. The map of the district is shown on Figure 4.0. 40 Figure 4.0: Map of Accra Metropolis 4.3 Selection of Participants for the Study This section describes how participants were selected for the study. This sub-sections discussed here contain description of the population for the study, sample size and sampling techniques for selecting participants. 4.3.0 Population of the Study The population of interest included online shoppers within selected district. A research population is a well-defined collection of individuals who share similar characteristics based on what a researcher is interested in and therefore qualify to be included in the study (Gravetter & Forano, 2018). Within the context of research, a population is defined as a group of individuals taken from the general population who share a common characteristic, 41 such as age, sex, or work conditions who are researched on because of their relevance to a research question (Bryman & Bell, 2015). In terms of research about consumer behaviour, the population is defined to include all individuals who patronise a particular good and or service, depending on the researcher’s interest (Bryman & Bell, 2015). In this study, the interest was to examine consumer perceived risks and online shopping behaviours among online shoppers. 4.3.1 Sample Size In this study, a total of two hundred and fifteen (215) online shoppers were surveyed for the study but responses from two hundred (200) online shoppers were used for the final analysis due to reasons of missing data. Sample is defined as the proportion of a population that is selected for research (Bryman & Bell, 2015). Thus, within the context of research, a sample size defined as a section of accessible population that is selected and studied (Creswell & Creswell, 2017). Two reasons account for why a sample is selected and studied. One is that, in most cases, especially in social science research, it is impossible or impractical to study the entire population (Creswell & Creswell, 2017). The second reason is that, it is possible to select a portion of the population to study and inferences made about the entire population of interest (Bryman & Bell, 2015). Sample size determination is therefore an important component of research. Using the right sample size ensures that a true picture is gained about the entire population of interest. As Nardi (2018) explains, both under-sampled and over-sampled studies all constitute a waste of resources for not having the capability to produce useful results. Because of this, sample size determination is very important, especially in survey research. Therefore, for the purposes of practicality, a sample size is always selected from the population and used for the research. However, the 42 sample size should be large enough so that the findings from the study can be generalized to the entire population (Bryman & Bell, 2015). This sample size is determined to be an adequate sample using different strategies for calculating sample size. For instance, some researchers recommend using power analysis to sample participants in studies that use inferential statistics to test hypothesis (Nardi, 2018). For instance, according to Tabachnick and Fidell, (2007), using power of 0.80 and alpha (α) value of 0.01, the minimum sample size required to detect a medium effect in a population about five thousand (5000) is one hundred and fifty-seven (157) participants. Nardi (2018) also recommends that there is the need to increase the minimum estimated sample size both to account for cases of non-response and to increases the likelihood that the sample statistic was representative of its corresponding population parameter. Therefore, the sample size of 200 online shoppers is adequate sample for inferential statistical analysis for testing hypotheses to aid in generalization of findings. The demographic characteristics of the respondents are provided in chapter five. 4.3.2 Sampling Technique In the study, the non-probability sampling procedure was used. Specifically, the online shoppers were selected using convenience sampling technique. A convenience sampling technique has an element of flexibility in the selection of research participants such that participants who are available at the time of data collection and are ready to take in the study are used (Patten & Newhart, 2017). The online shoppers were therefore selected based on their availability and voluntary participation. For the purposes of inclusion and exclusion criteria, only online shoppers who were willing to participate in the study were included in the selection process. 43 The processes involved in selecting part of a population for research is called sampling technique (Bryman & Bell, 2015). Sampling technique thus refers to the processes and procedures involved in the selection of participants from portion of population to study. The main goal of sampling is to get a sample that is fairly representative or a smaller collection of units from a much larger collection or population, study the smaller group and produce accurate generalizability about a larger group. The right sample size depends on the nature of a population and the purpose of the study. There are two broad categorizations of sampling techniques. These include probability sampling and non-probability sampling techniques (Creswell & Creswell, 2017). Probability sampling is the type of sampling procedures where each member of the population has equal chance of being selected for the study. When it comes to probability sampling, researchers do not have control over who is selected and it allows for representative cross sections, or particular groups to be identified or targeted (Patten & Newhart, 2017). Probability sampling includes simple random sampling, systematic sampling, cluster sampling and stratified sampling. Non-probability sampling on the other hand is a sampling procedures where the members of a population do not have equal chances of being selected (Gravetter & Forzano, 2018). The defining feature between these two sampling strategies or techniques is the extent or chance by which each member of the population has for being part of the study. There are different techniques that are used in the selection samples using non-probability sampling. This include purposive sampling, convenience sampling, snow balling, quota sampling etc. (Bryman & Bell, 2015). 44 4.4 Measures for Data Collection Measures for data collection are various methods such as survey questionnaire, observation, and interview that are used to obtain data, from study participants. In cross-sectional survey design, the data collection instrument is the questionnaire that is used to gather data from the research participants. According to Nardi (2018), questionnaires are the most widely used research tool because it is practical. Large amounts of information can be collected from a large sample size in a short period of time and it is also cost effective. Therefore, in the study, the data was collected using an already developed questionnaires but was adapted to suit the Ghanaian context. The questionnaire had different sections with each of the sections measuring different aspects of attitudes and use of online shopping behaviours. The sections are described below: Demographic Information: this section of the questionnaire elicited information such as gender, age and marital status activities and educational level. Online Shopping Use: this section elicited information on the various online shopping behaviours that respondents have engaged in. Questions elicited information such as how long they have been using online shopping and they usually use online shopping for. Consumer Perceived Risks: this section elicited information on the online shoppers’ perception the risks associated with online shopping behaviours in Ghana. The main perceived risk factors examined based on the theoretical framework were; financial risk, privacy risks, product risk, delivery risks and process and time loss risks. Participants were required to respond on a five-point Likert scale ranging from 1 – 5, where1= Strongly Disagree, 2 = Disagree, 3 = Somehow, 4 = Agree, 5 = Strongly Agree. Online shopping behaviour: this section elicited information on the participants’ perceptions and attitudes towards online shopping. Items here elicited information including 45 access to product brands, product quality, access to product information etc. Participants were required to respond on a five-point Likert scale ranging from 1 – 5, interpreted as follows; 1= Strongly Disagree, 2 = Disagree, 3 = Somehow, 4 = Agree, 5 = Strongly Agree. E-word of mouth: this section elicited information regarding the things they hear about online shopping from their social world including celebrities, family, friends, colleagues etc. Participants were required to respond on a five-point Likert scale ranging from 1 – 5, where 1= Strongly Disagree, 2 = Disagree, 3 = Somehow, 4 = Agree, 5 = Strongly Agree 4.5 Procedures for Data Collection The study began by first obtaining an introductory letter from the Department of Marketing and Entrepreneurship of the University of Ghana Business School. After that, a pilot study was first conducted with thirty (30) online shoppers. These online shoppers were contacted by visiting two (2) online shop companies namely Tisu and Zoobashop at their premises in Accra. The pilot study served the purpose of making sure that the items in the questionnaire made sense to the online shoppers before the main data was collected. After the pilot study, the scales were found to be useful for the study which gave the researcher the power to collect the main data. In process of the main data collection, the researcher and two research assistants (who were undergraduate students) visited some online store companies and other organizations and tertiary institutions and distributed questionnaires to online shoppers. The participants were identified by first engaging in an interaction with them. After the initial visit of distributing the questionnaires, the research assistants then visited the online shoppers on daily basis in the time and day agreed by the online shoppers to retrieve the questionnaires. After the time 46 period, it took the researcher almost a month to retrieve the questionnaires from the respondents. 4.6 Ethical Consideration Ethics are very critical aspects of research. Ethical considerations are meant to ensure that participants in any kind of researched are protected and free from exploitation at all times. According to Nardi (2018) all researchers are advised to adhere to some professional ethical codes and regulations while undertaking research. Creswell and Creswell (2017) also argues that researchers participants need to be protected to promote the integrity of research, develop a trust with them, guard against misconduct and impropriety that might reflect on their organizations or institutions and cope with new and challenging problems. In view of this, higher standards of ethical considerations were strictly adhered to in accordance with the ethical principles governing the use of human participants for research purpose. Consent was sought from all online shoppers contacted before data was collected. In the consent seeking process, the aims and scope of the study were explained to them. The researcher also ensure high sense of confidentiality and anonymity by making sure the data collected is managed in such a way that the identities of the respondents were protected at all times and that no information were directly traced or associated with any individual participant. With this, no names or codes traceable to the respondents were used. Those who agreed to be part of the study were given the questionnaire to fill. 47 4.7 Data Analysis The data for the study was processed and analysed using of Statistical Package for Social Sciences (SPSS) software version 23. The hypothesized relationships were analysed using Structural Equation Modelling (SEM). All the responses from the participants were first of all coded into the SPSS software. The data was first analysed using descriptive statistics such as frequency tables. Details are provided in the presentation of results in chapter five. 48 CHAPTER FIVE DATA PRESENTATION, ANALYSES AND DISCUSSIONS OF FINDINGS 5.0 Introduction The study examines perceived risk factors that act as barriers to online shopping among online shoppers in Ghana. Five perceived risk factors were set; financial risk, privacy risk, product risk, delivery risk and process and time loss risk. The moderating effect of e-word of mouth between perceived risk and online shopping behaviour were also assessed. This chapter presents findings based on the objectives of the study. The chapter is presented as follows. First, the demographic characteristics of the participant online shoppers are presented. After that, online shopping activities among the participants are presented. Descriptive statistics of scores among the participants are provided afterwards. Model testing procedures using Structural Equation Modelling (SEM) for testing the hypotheses are then presented. A summary of the findings are also presented and discussed. 5.1 Data Analysis 5.1.0 Demographic Profile of Respondents The demographic profile of the online shoppers used in the study are presented in this section. In all, a total of two hundred (200) online shoppers took part in the study. Their demographic profiles are presented on Table 5.0. 49 Table 5.0: Demographic profiles of participants Demographic Factor Category F % Gender Male 124 62.0 Female 76 38.0 Marital Status Single 154 77.0 Married 45 23.0 Age ≥ 20 years 16 10.7 21 – 30 years 120 60.0 31 – 40 years 65 32.5 41 - 50 years 15 7.5 Education SHS 15 4.13 Diploma 45 22.5 First Degree 115 57.5 Master’s Degree 25 12.5 Status Student 90 45.0 Government Worker 40 20.0 Private Organization 55 27.5 Self-employed 10 5.0 Unemployed 5 2.5 Source: Field Data, 2018 As shown on Table 5.0, more than half of the participants were males (62%), with the remaining 38% being females. The sample for the study was relatively young, their ages ranged between 20 and 50 years, with majority of them falling between the ages of 21 – 30 50 years. Accordingly, majority of them reported their marital status as single (77.0%), with the remaining 23% being married. The participants also had relatively high educational level. The minimum level of education was Senior High School (SHS) and the highest level of education was masters’ degree. Majority of the participants had attained first degree qualification (57.5%). Most of the participants (45%) were students, followed by those who work in private organization (27.5%), government workers (20%), self-employed (5%) and unemployed (2.5%). 5.2 Online Shopping Activities This section presents participants’ engagement with online shopping behaviours. The participants were made to indicate how long they have been using online shopping platform and what they usually do with shopping online. Their responses are presented on Table 5.1. The findings, as shown on Table 5.1 show that the respondents have been shopping online mainly in the last five years, with majority of the respondents have been shopping online in the last 2 years (57.5%). The main activities respondents’ use online shopping for buying items online (95%), searching for product information online (60%), comparing prices of products (57.5%) and few of them also sell items online (10.7%). Table 5.1: Online shopping behaviours of participants Online shopping behaviours Responses f % How long have you been shopping online? 1 – 2 years 115 57.5 51 3 – 4 years 75 32.5 5+ years 20 10.0 What did you use the online shopping for? Buying items 190 95.0 Selling items 16 10.7 Searching for product 120 60.0 information Comparing prices of products 115 57.5 Source: Field Data, 2018 5.3 Descriptive Statistics of Scores This section provides descriptive statistics of scores on the various items in the questionnaire used in the survey. The descriptive statistics helps in visualising distribution of scores of the participants, which serves critical relevance in inferential statistical analysis (Pallant, 2013; Tabachnick & Fidel, 2007). The descriptive statistics presented here are mainly means and standard deviations of each of the items. In all, there were sixty-four (64) items in the questionnaire, measuring seven (7) different constructs or variables. The results are summarized on Table 5.2. Table 5. 2: Descriptive Statistics of Scores Scale items Code Mean SD Financial Risk I can’t trust the online company FR1 3.48 .93 I may not get the product FR2 3.14 .80 52 I may purchase something by accident FR3 2.40 .98 My personal information may not be kept FR4 3.33 .73 I may not get what I want FR5 4.27 .83 My credit card number may not be secure FR6 4.19 .81 I might be overcharged FR7 4.28 .82 Traditional stores offer more discount than online store. FR8 3.60 .95 Online stores offer discount price but the total cost is not lower. FR9 3.88 .96 Online payment will charge extra fees. FR10 4.17 1.05 Delivering to the home will charge relatively higher fees. FR11 3.85 1.16 I might lose my money FR12 4.13 .84 Product Risk The quality of the product is not accepted. PR1 3.85 1.15 The product performance is not consistent with the expectation. PR2 3.86 1.16 The product may be false and the quality will be poor. PR3 4.07 .76 It is difficult to return when the product is not satisfied. PR4 3.89 1.17 Cannot try on clothing online. PR5 4.07 .92 Unable to touch and feel the item. PR6 3.89 .87 Size may be a problem with clothes. PR7 4.10 .97 Cannot examine the actual products. PR8 3.89 1.15 The product might fail to perform to my satisfaction. PR9 4.14 .95 Can’t examine the actual product PR10 3.93 .84 Size may be a problem with clothes PR11 4.01 .99 Can’t try on clothing online PR12 2.37 .98 Inability to touch and feel the item PR13 3.95 .79 53 Must pay for shipping and handling PR14 2.49 .93 It is difficult for me to judge products' quality adequately. PR15 3.14 .80 It is difficult for me to compare the quality of similar products. PR16 2.40 .98 The product purchased may NOT perform as expected. PR17 3.33 .73 Must wait for merchandise to be delivered PR18 4.27 .83 Delivering risk Delivered the product to a wrong place. DR1 4.28 .82 The product is damaged during the delivering. DR2 3.60 .94 The delivered product could be lost. DR3 3.88 .96 Process and Time Loss Risk The process of online shopping is complex and inconvenient. PTR1 4.18 .81 To deal with PC for accessing Internet will take too much time. PTR2 3.14 .80 Information transformation is too slow during online shopping. PTR3 2.40 .98 I might waste my time getting the product repaired or replaced. PTR4 3.32 .72 Too complicated to place order. PTR5 4.27 .82 Difficult to find appropriate websites PTR6 3.60 .94 Pictures take too long to come up PTR7 4.28 .82 Privacy risk Shopping on the internet jeopardises my privacy PvR1 3.11 .96 Internet shopping is more risky than shopping in a store. PvR2 4.12 .82 My credit card number may not be secure PvR3 3.61 .94 Personal address, telephone number could be misused by PvR4 3.12 .96 others. Online retailers may track my shopping habits and history PvR5 3.31 .82 54 Online Shopping Behaviour I usually shop online OSB1 3.21 .73 In online individuals get access to information OSB2 4.11 .83 In online individuals gets wherever they want. OSB3 3.23 .95 Online saves the effort of visiting stores OSB4 4.12 .82 Online Items from everywhere are available OSB5 3.22 .75 Can get good product information online OSB6 3.88 .96 Online provides broader selection of products. OSB7 4.02 .82 Access to many brands and retailers in online OSB8 3.04 .94 Don’t have to wait to be served in online OSB9 2.49 .93 No hassles OSB10 3.14 .81 Not embarrassed if you don’t buy in online OSB11 2.40 .98 No busy signal in online OSB12 3.33 .73 E-word of mouth Celebrity endorsements influence my decision to shop online EM1 3.31 .74 People who are important to me expect me to shop online EM2 4.27 .81 Reputation of the web site influence my purchase decision EM3 3.60 .95 I think that my colleagues expect me to shop online EM4 4.28 .82 Special offers / sales promotions influence my purchase EM5 3.41 .71 decision Peer group (my friends) influence my decision to purchase EM6 3.34 .96 online Family members influence my decision to purchase online EM7 4.11 .82 Source: Field Data, 2018 55 From Table 5.2, the highest mean score was 4.28 which were recorded on four items; one on financial risk (I might be overcharged), one on delivery risk (Deliver the product to a wrong place), one on process and time loss risk (Pictures take too long to come up), and the other on e-word of mouth (I think that my colleagues expect me to shop online). The lowest mean score was 2.37, on product risk (Can’t try on clothing online). This gives an indication that relatively, the participants have higher sense of perceive risk with regard to shopping online in Ghana. 5.4 Structural Equation Modelling Procedures This section discusses the detailed analytical processes and procedures in testing the hypotheses in the study. The data was analysed using SEM with the help of IBM’s SPSS version 23 and AMOS version 21. The structural modelling procedures involved Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and model testing. The procedures are described next. 5.4.0 Exploratory Factor Analysis Exploratory Factor analysis (EFA) was first conducted on the items in the scale to assess underlying structure of the variables within the Ghanaian context. Specifically, Principal Component Analysis (PCA) was used with Eigen values greater than 1. It was expected that all items that measure the same construct load onto the same factor (Tabachnick & Fidell, 2007). Factor loadings were set at 0.40 for all the items. Assumption of sampling adequacy was tested using Kaiser-Meyer-Olkin (KMO) test. The acceptable level of KMO should be > 0.6 (Kaizer, 1970). The number of factors to be extracted was determined using eigen values 56 greater than 1 and scree plot (Pallant, 2013). The correlation matrix indicated several coefficients with high acceptable values of 0.4 and above. The sampling adequacy assessment using KMO test was found to be significant (KMO = .816, χ2 = 3185.117, p < .001). After inspecting eigen values of factors exceeding 1 and the scree plot, where it was observed to levelled out after the seventh factor. Seven (7) factors were therefore extracted. Data reduction was undertaken to improve on the analysis. Therefore, some items were taken out. These included items that either loaded weakly (< .05), items that did not load on any of the factors and cross loading items (Tabachnick & Fidell, 2007). The results of extracted factors and their properties are summarized on Table 5.3. Table 5. 3: Rotated Component Matrix Table Principal component loadings Internal consistencies Factors Item codes Varimax Variance α Item-total α if Loadings Explained correlation deleted Factor 1 FR1 .938 73.211 .817 .525 .685 FR2 .937 .521 .685 FR4 .637 .584 .801 FR5 .956 .657 .769 FR6 .950 .579 .714 57 FR7 .844 .662 .716 FR8 .774 .678 .714 FR9 .526 .410 .707 Factor 2 PR2 .600 68.112 .795 .582 .700 PR3 .756 .526 .685 PR4 .734 .596 .707 PR5 .737 .411 .706 PR6 .721 .660 .709 PR7 .744 .420 .719 PR8 .725 .582 .721 PR9 .730 .479 .708 Factor 3 DR1 .788 66.611 .865 .621 .628 DR2 .727 .680 .835 Factor 4 PTR1 .626 69.321 .863 .525 .685 PTR2 .901 .521 .685 PTR3 .843 .457 .385 PTR4 .777 .627 .793 Factor 5 PvR1 .657 72.221 .819 .474 .526 PvR2 .701 .533 .776 PvR3 .689 .400 .652 PvR4 .584 .428 .646 PvR5 .657 .522 .673 Factor 6 OSB1 .579 70.032 .811 .537 .608 OSB2 .662 .456 .637 58 OSB3 .678 .353 .664 OSB4 .646 .485 .684 OSB5 .673 .521 .723 OSB6 .600 .489 .734 Factor 7 EM1 .774 67.431 .843 .664 .721 EM2 .526 .684 .711 EM3 .776 .623 .716 EM4 .652 .680 .679 EM5 .646 .680 .716 Source: Field Data, 2018 From Table 5.3, a total of 36 items were judged to have been loaded very well. Specifically, when it comes to the perceived risk factors; eight (8) items from financial risk were maintained, eight (8) were maintained for product risk, all five (5) were maintained for privacy risk, four (4) items were maintained for process and time loss risk and two (2) items maintained for delivery risk. For the dependent variable (online shopping behaviour), six (6) items were maintained and five (5) were also maintained for the moderating variable (e-word of mouth). Internal consistencies for the seven (7) extracted factors were examined using both Cronbach alpha coefficient (α) and item-total correlation values. For accepted level of reliability, Cronbach alpha coefficient (α) should be > 0.70 (Tabachnick & Fidell, 2007). As shown on Table 4.5, all the factors recorded high reliability, with Cronbach alpha coefficient (α) ranging between 0.795 and 0.865. The extracted factors also explained high variances, ranging between 66.111% and 73.211%. 59 5.4.1 Confirmatory Factor Analysis When the EFA, was successfully completed, Confirmatory Factor Analysis (CFA) was undertaken. The codes of successful factor-loading items from the EFA were used for the CFA analysis. Model fit was assessed using the commonly use model fit indices; Root Mean Square Error of Approximations (RMSEA), adjusted goodness-of-fit index (AGFI), Goodness-of-fit index (GFI), comparative Fit Index (CFI), Tucker Lewis Index (TLI), Chi Square Degree of Freedom Rotation (CMIN/DF), and Chi Square goodness of fit (χ2). The model fit indices examines different aspects of model testing procedures. For instance, the CFI examines whether the final model being considered is better than other competing models in the observed data (Pallant, 2013). The RMSEA for instance also assess the extent by which the model being considered is the best fit for the population under study (Byrne, 2016). The GFI also examined the fit between the hypothesized model and the observed covariance matrix (Tabachnick & Fidell, 2007). The sufficiency of the model is evaluated using χ2 goodness of fit, which also estimates the coefficients compared with the covariance matrix (Kline, 2015). Given that the χ2 is influenced by sample size, care needs to be taken when assessing the model fit since a large sample size can cause the χ2 to be inflated (Marsh, Morin, Parker & Kaur, 2014). Based on this, some scholars argue that the χ2 needs to be divided by the degree of freedom to take care of the possibility of being inflated by large sample size. In this sense, CMIN/DF is also provided for assessing model fit (Marsh et al. 2014). The model fit indices have their threshold parameter estimates for evaluating a model. The estimates are summarized on Table 5.5. Table 5. 4: Parameter estimate threshold for model fit indices Fit Index Parameters 60 RMSEA < .06 CMIN/DF Between 1 and 3 χ2/df < 2 CFI > .95 TLI > .95 SRMR < .08 PCLOSE Should not be significant ( > .05) Source: Kline, 2015; Marsh et al. 2014, Tabachnick & Fidell, 2007 61 These threshold parameter estimates on Table 5.4 of the fit indices were used to evaluate the final measurement model (Figure 5.0). Figure 5.0: Final measurement model The final measurement model was achieved after some modification which mainly involve co-varying some items. Financial represents perceived financial risk, product represents 62 perceived product risk, privacy represents perceived privacy risk, time represents perceived process and time loss risk, delivery represents perceived delivery risk, online represents online shopping behaviour and E-word represents e-word of mouth. The results of the final measurement model is provided on Table 5.5 Table 5. 5: Fit indices of final measurement model Fit Index Parameters Final model estimates Interpretation CMIN - 172.104 DF - 121 RMSEA < .06 0.021 Excellent CMIN/DF (χ2/df) Between 1 and 3 1.872 Excellent CFI > .95 0.967 Excellent TLI > .95 0.952 Acceptable SRMR < .08 0.051 Excellent PCLOSE Should not be 0.344 Acceptable significant ( > .05) Source: Field Data, 2018 From Table 5.5, the final measurement model achieved good fit on various indices. Other fit indices which were also generated included AGFI = 0.942, GFI = 0.962 and IFI = 0.973. All these indices are consistent with acceptable levels of model fit (Kline, 2015; Marsh et al. 2014, Tabachnick & Fidell, 2007). Correlations among the factors 63 After the final model fit, correlations among the factors were examined to assess the relationships existing among the variables using Pearson r test. The variables included online shopping behaviours, components of customer perceived risk (i.e. financial risk, product risk, delivery risk, privacy risk, process and time loss risk) and e-word of mouth. This was done to provide basis for later causality analysis using path regression analysis (Pallant, 2013). The results are provided on Table 5.6. Table 5. 6: Correlations for CFA and SEM Analysis Observed Variables 1 2 3 4 5 6 7 1. Online shopping behaviour 1 2. Financial risk -.603*** 1 3. Product risk -.501*** .604** 1 4. Privacy risk .-629*** .106 .532** 1 5. Delivery risk -.353** .030 .199* .560** 1 6. Process and time loss risk -.309** -.251** -.267** -.180* -.126 1 7. E-word of mouth .390** .304** .300** .333** .334** -.214** 1 Source: Field Data, 2018; * p < .05; ** p < .01; *** p < .001 The results show that online shopping behaviour is significantly negatively correlated with all the components of perceived risk. For instance, as shown on Table 5.6, online shopping behaviour has significant negative relationship with financial risk (r = -.603, p < .001), product risk (r = -.501, p < .001), privacy risk (r = -.629, p < .001), delivery risk (r = -.353, p < .01) and process and time loss risk (r = -.309, p < .01). It however has significant positive relationship with e-word of mouth (r = .390, p < .01). 64 Validity of Final Model After examining correlations among the factors, convergent validity among the factors were also assessed. Convergent validity examines the extent to which the items of a latent variable share their covariance (Kline, 2015). This was assessed using Average Variance Extracted (AVE) and Composite Reliability (CR). CR has been argued to be less biased estimate compared to Cronbach alpha (Marsh et al. 2014). AVE examines the variance level accounted for by a construct compared to the level accounted for by measurement error. For convergent validity to be achieved, CR values should be 0.7 or more and AVE values above 0.7 are considered very good, but the level of 0.5 is acceptable (Kline, 2015; Tabachnick & Fidell, 2007). The results are provided on Table 5.7. Table 5. 7: Validity and reliability of final model Factors Items Loadings CR AVE α Factor 1 FR1 .938 0.808 .711 .817 FR2 .937 FR4 .637 FR5 .956 65 FR6 .950 FR7 .844 FR8 .774 FR9 .526 Factor 2 PR2 .600 0.788 0.695 .795 PR3 .756 PR4 .734 PR5 .737 PR6 .721 PR7 .744 PR8 .725 PR9 .730 Factor 3 DR1 .788 0.866 0.784 .865 DR2 .727 Factor 4 PTR1 .626 0.859 0.805 .863 PTR2 .901 PTR3 .843 PTR4 .777 Factor 5 PvR1 .657 0.799 0.754 .819 PvR2 .701 PvR3 .689 PvR4 .584 PvR5 .657 Factor 6 OSB1 .579 0.802 0.768 .811 66 OSB2 .662 OSB3 .678 OSB4 .646 OSB5 .673 OSB6 .600 Factor 7 EM1 .774 0.846 0.778 .843 EM2 .526 EM3 .776 EM4 .652 EM5 .646 Source: Field Data, 2018 From Table 5.7, no validity concerns were identified. All the reliability measures were above the acceptable levels; CR was between 0.788 – 0.966, AVE was between 0.695 – 0.805 and α between 0.795 – 0.865. All the factors loadings also showed good convergent validity. The final model is thus regarded as the model that adequately fits the data for the current study. 5.4.2 Testing Hypothesized Relationships After testing the validity and reliability of the final model, the hypothesized relationships were then tested. The direct effect hypothesis were tested first before the moderation hypothesis. The direct relationship hypotheses tested are summarized below: H1: Perceived financial risk is negatively related to online shopping behaviour 67 H2: Perceived privacy risk is negatively related to online shopping behaviour H3: Perceived product risk is negatively related to online shopping behaviour H4: Perceived delivery risk is negatively related to online shopping behaviour H5: Perceived process and time loss risk is negatively related to online shopping behaviour The moderation hypothesis tests were summarized below: H6: E-word of mouth moderates the relationship between perceived risk and online shopping behaviour The moderation effects were tested using the procedures recommended by Baron and Kenny (1986). A composite score was obtained for consumer perceived risk by adding the scores of all the components together. The continuous predictor and moderator variables were centred as recommended by Jose (2013) to reduce multicollinearity between the main effect and interaction terms. Therefore, the predictor and the moderator variable were first of all centred. The centring was done by subtracting the mean of each variable from the score of each participant (Pallant, 2010; Tabachnick & Fidell, 2007). After that, an interaction term (IV) was created by multiplying the centred IV with the centred moderator variable (MV). Moderation is judged to have occurred if the interaction term is significant. The fit indices obtained from testing the hypothesized relationships are provided on Table 5.8. Table 5. 8: Fit indices for hypothesized relationships Fit Index Parameters Hypothesized model estimates Interpretation CMIN 174.443 68 DF 121 RMSEA < .06 0.048 Acceptable CMIN/DF (χ2/df) Between 1 and 3 1.899 Excellent CFI > .95 0.956 Acceptable TLI > .95 0.957 Acceptable SRMR < .08 0.048 Excellent PCLOSE > .05 0.248 Acceptable Source: Field Data, 2018 Other fit indices generated included AGFI = 0.954, GFI = 0.960 and IFI = 0.971. All these indices are consistent with acceptable levels of model fit (Kline, 2015; Marsh et al. 2014, Tabachnick & Fidell, 2007). The significance of the hypothesized relationships are summarized on Table 5.9. Table 5. 9: Summary of significance of hypothesized relationships Hypotheses Regression Path β t P Decision H1 Financial risk -> Online shopping .551 7.127 .000 Supported behaviour H2 Privacy risk -> Online shopping -.183 -2.868 .005 Supported behaviour H3 Product risk -> Online shopping -.028 -.369 .713 Not supported behaviour 69 H4 Delivery risk -> Online shopping .185 2.762 .007 Supported behaviour H5 Time loss risk -> Online shopping -.045 -.431 .654 Not supported behaviour H6 E-word of mouth moderating the effect Supported of perceived risk on online shopping .185 2.762 .007 behaviour Assessment of Direct Relationship between Perceived Risk Factors and Online Shopping Behaviour From Table 5.9, significant direct relationships is achieved between three perceived risk factors (i.e. financial risk, privacy risk and delivery risk) and online shopping behaviours. Specifically, financial risk had a significant negative impact on online behaviour (β = -.551, t = 7.127, p <.001). This means that a unit increase in perceived financial risk leads to a .551 standard deviation decrease in their online shopping behaviours. Privacy risk also had significant negative effect on online behaviours (β = -.183, t = -2.868, p < .01). This means that a unit increase in the perceived privacy risks leads to a .183 standard deviation decrease in their online shopping behaviours. 70 Also, delivery risk had significant negative effect on online behaviours (β = -.185, t = -2.762, p <.01). This means that a unit increase in the perceived delivery risks leads to a .185 standard deviation decrease in their online shopping behaviours. However, both product risk (β = -.028, t = -.369, p = .713) and process and time loss risk (β = -.045, t = -.431, p = .654) did not have significant impact on online shopping behaviours of the participants. Assessment of Moderation Roles of E-word of mouth From Table 5.10, E-word significantly moderated the association between consumer perceived risk and online shopping behaviours (β = .185, t = 2.762, p < .01). A unit increase in e-word of mouth reduces the negative impact of perceived risk on online shopping behaviours by about .185 standard deviations. 5.5 Observed Model The findings from the study are summarized on the observed model (Figure 5). As it can be seen, three perceived risk factors (i.e. financial risk, privacy risk and delivery risk) predicted online shopping behaviours. The effect of perceived risk on online shopping behaviours was moderated by e-word of mouth. 71 Figure 5.1: Observed Model of Predictors and Moderators of Online Shopping Behaviours Source: Field Data, 2018 5.6 Discussion of Findings This section presents a discussion of the findings by integrating them together to make sense of how the online shoppers orient themselves towards online shopping behaviours and how that shape whether or not they would accept or not accept it. The findings are synthesized and discussed around two organizing themes; (i) perceived risk factors and online shopping behaviours and (ii) moderating role of e-word of mouth. 72 5.6.0 Perceived Risk Factors and Online Shopping Behaviours The main objective of the study was to examine the perceived risk factors that influence the adoption or acceptance of online shopping behaviours among the online shoppers. Based on the literature, five main perceived risk factors were assessed. They were financial risk, product risk, privacy risk, delivery risk and process and time loss risk. Findings revealed that three elements of perceived risk factors (i.e. financial risk, privacy risk and delivery risk) affected online shopping behaviours. These factors mean that when the online shoppers perceive that transaction with online shopping behaviours is financial risky, the online shoppers are less likely to accept it. When they believe that online transactions can invade their privacy, the online shoppers are less willing to accept it. Again, when they perceive risk in the delivery of the products they purchase online, they are less likely to engage in online shopping. The findings mean that before the online shoppers accept or adopt online shopping behaviours, they have to develop a positive or favourable attitude about the innovation in financial transaction. The findings are in line with other related studies that examined technological acceptance in general and cashless payment system acceptance in particular in other places (Faqih et al. 2015; Khanna, 2017; Kurnia et al. 2015; Liébana-Cabanillas et al. 2018) and also in Ghana (Awiagah et al. 2016; Boateng, 2011; Boateng et al. 2011; Frimpong & Gyamfi, 2016). The findings in this study provide support for some earlier studies and at the same time contradict other studies. A study by Al-Smadi (2012) assessing theory of planned behaviour in predicting internet banking for instance found that attitudes and perceived behavioural control significantly affected the intentions of the customers to use internet banking. This means that when 73 individuals develop positive attitudes towards electronic banking and have the necessary or the needed facility to access it, they are more likely to use e-banking. Awiagah et al., (2016) have also reported similar findings when they investigated factors affecting electronic commerce adoption among small businesses in Ghana. The findings from their study showed that perceived risk barriers prevent customers from using mobile banking services. The customers were found to be aware of the efficiency of mobile banking services, but still preferred traditional banking services. In another study in Ghana, Domeher et al., (2014) found that perceived usefulness, compatibility and lack of complexity significantly predicted financial innovation uptake. Also when consumers believe that shopping online their personal information’s can be disclose to the third party consumers then turn not to purchase online this findings is consistent with other studies that have examine privacy risk as a factor that hinder online transactions (Metzger, 2004; Vijayasarathy & Jones, 2000; Dillon & Reif, 2004). 5.6.1 Moderation Role of E-word of Mouth The study also examined moderating role of e-word of mouth between perceived risk factors and online shopping behaviours. Findings showed that e-word of mouth moderate the effect of consumer perceived risk on online shopping behaviours, such that increase in e-word of mouth reduces the negative impact of perceived risk on online shopping behaviours. This means that when consumers hear good testimonies of online shopping within their social world, their perceived fear reduces which can increase their online shopping behaviours and also important purchase decisions. The study therefore makes that contribution to theory and literature on consumer behaviour in relation to technology adoption in Ghana. 74 The findings in the study, and those from related studies point to the fact that in developing countries in general, adoption or acceptance of technological innovations in general and online transactions in particular is very complex and problematic. This is especially so among low income individuals with low levels of education who always perceive high risk associated with technological advancement when it comes to financial transaction. The online shoppers in the current study therefore are concerned with financial, privacy and deliver risks when it comes to online shopping. This provide entry point to e-commerce companies in Ghana on how best to reach out and encourage the general public into adopting online shopping. 5.7 Chapter Summary This chapter has provided the analysis and findings from the study. The main findings have been integrated and discussed under three organizing themes to make sense of how the online shoppers make sense of online shopping behaviours and how the attitudes they have developed influence acceptance among them. 75 CHAPTER SIX SUMMARY, CONCLUSION AND RECOMMENDATION 6.0 Introduction The current study therefore examined how different components of consumer perceived risk influence their online shopping behaviours. This is to help bridge the gap in knowledge in online shopping behaviour research in particular and consumer behaviour research in general in a developing country contexts. Five main objectives were examined. This chapter provides the summary of findings, conclusions and practical and theoretical recommendations based on the findings from the study. 6.1 Summary of the Study Technological innovations has come with it advancements in all spheres of life. One area that technological advancement has brought improvement in is commerce, the act of buying and selling. Online shopping, as a result of technological innovation has brought convenience in shopping behaviours. Customers can now sit in the comfort of their homes and offices to buy goods and have them delivered without any physical stress of moving around town. While online shopping is well advanced and booming in high income countries, the same cannot be said of low and middle income countries. In Africa, online shopping has not caught up with the populace, even though it is gradually doing so. But the progress has been very slow. In Ghana for instance, even among high income and elite population in the national capital, traditional shopping is still dominant. This has generated increased research attention to understanding online shopping behaviours in 76 developing countries to help in promoting it acceptance. In high income countries, at the early stages of adopting online shopping, risk perceived by the potential consumers was a serious hindrance. This is under researched among online shoppers in developing countries. The findings from the study are summarized in this section. In all, it was realized that: i. Participants were found to hold generally positive attitudes towards online shopping: a. The participants have been engaging in online shopping behaviours in the last five years, with majority of them shopping online in the last two years. b. The participants were found to hold largely positive attitudes and perceptions about online shopping. ii. In terms of differences in online shopping behaviours based on demographic factors: a. There was significant gender differences. Male participants were found to be more accepting of online shopping compared to female participants. b. No significant differences in educational level were found in online shopping behaviours. c. However, there was a significant interaction effect between gender and educational level on acceptance of online shopping. Specifically, male participants who have First degree and above education are more accepting of online shopping than male participants who have below first degree. iii. Consumer perceived risk had significant effect on online shopping behaviours among the participants. Specifically; a. Financial risk had a significant negative impact on online behaviour, such that an increase in perceived financial risk leads to a decrease in their online shopping behaviours. b. Privacy risk also had significant negative effect on online behaviours, such that an increase in the perceived privacy risks leads to a decrease in their online shopping behaviours. 77 c. Delivery risk had significant negative effect on online behaviours, such that an increase in the perceived delivery risks leads to a decrease in their online shopping behaviours. d. However, both product risk and process and time loss risk did not have significant impact on online shopping behaviours of the participants. e. Perceived financial risk had the greatest effect on online shopping behaviours among the participants. iv. E-word of mouth significantly moderated the effect of perceived risk on online shopping behaviours, such that an increase in e-word of mouth reduces the negative impact of perceived risk on online shopping behaviours. Generally consumer perceive risk had a negative influence on online shopping behaviour in the sense that when consumers perceive high risk associated with online shopping it affect their online shopping behaviour this findings, is in line with other studies that concern online shopping (Bhatnagar, 2004; Caudill & Murphy,2000; Culnan, 1999; Gauzente, 2004; Hoffman et al., 1999; Milne & Culnan, 2004). Consumer perceived risk has a direct negative influence on consumer behavior with regards to online shopping (Vijayasarathy & Jones, 2000; Yeung & Morris, 2006). It also affects the online purchasing decision (Dillon & Reif, 2004), and the volume of online purchases (Miyazaki & Fernandez, 2001; Doolin et al., 2005). Also the findings are consistent with studies by scholars such as Featherman et al. (2010), Biswas and Biswas (2004), and Hong-Youl (2004) all who found consumer perceived risk to be a major barrier to shopping online. 6.2 Conclusions of the Study The current study has shown that online shopping behaviours are affected by complex set of perceived risk factors. The factors range from mainly perceived financial risk, privacy risk, 78 delivery risk. This means that marketers and online shops have complex human behavioural factors to overcome in encouraging online shoppers. This requires investment in research into consumer behaviour within the context of technological adoption, especially in low and middle income countries. This will help them to know the best way to reach out to them with technologically-oriented online shopping products and services. The objective of the study was to determine the relationship between consumers perceived risk dimensions and online shopping behaviour among consumers in Ghana. The findings from the study indicate that consumer perceived risk dimensions namely financial risk, product risk and delivery risk had direct influence on online shopping behaviour among consumers in Ghana. Theoretically, this research has confirmed three important findings. Firstly, product risk, financial risk, and delivery risk negatively affect consumers behaviour towards of online shopping. These findings are consistent with several authors who found that online shopping behaviour to online, were affected by perceived risk (Masoud, 2013; Zhang, Tan, Xu, Y. & Tan, 2012). The possibility of shopping online decreases when product risk increases (Bhatnagar et al., 2000), and this view is supported by this research. The negative effects of perceived risk have also been found to have a negative impact on online shoppers behaviour towards shopping online (Van der Heijden et al., 2003; Shih, 2004). Financial risk had the highest negative influence affecting consumer behaviour. Thus, online managers should put more effort on minimizing financial risk in order to influence positive online behaviour to do online shopping with them. The perceived financial risk could be reduced by protecting customers spending pattern and personal information, avoiding misused of credit card details by third parties and eliminating overcharging price. In addition, product risk can be avoided by assuring the advertisers to sell quality products with clear pictures and description of the product. For delivery risk, it can be decreased by providing a place for online shopping 79 customers to pick up their products if they don’t receive the delivery items on time and prompt action should be taken on it. Perceive risk had negative impact on online shopping behaviour among online shoppers. Electronic word of mouth had significant impact on the moderating role between consumer perceived risk dimensions and online shopping in Ghana. For instance, positive electronic word of mouth such as recommendations from actual customers’ needs to be encouraged to influence consumers’ behaviour about online shopping. Online shopping companies ought to promote the possibility of linking and sharing the information created by integration of links from distinct social networks such as Twitter, Facebook, and comments from a blogs. The objective of the sellers and advertisers that want to benefit from the effects of online marketing ought to be to focus on platforms that tend to the informational needs of their user community and that have influential users. Online managers need to do more of online reviews to draw consumers attention on online shopping through products purchase by actual customers based on their positive comments on products. This requires investment into consumer behaviour within the context of technological adoption. This study has shown that, at least, there are certain perceived risk dimensions that affect online shopping behaviours in Ghana. 6.3 Recommendations Based on the experiences gathered from respondent provided significant findings from which lessons have been developed. This study therefore acknowledged the necessity of making recommendations for practitioners, policy makers and industry stakeholders on the issue of consumer perceived risk dimensions and online shopping in Ghana. 80 First, a more positive attitudes and perception towards online shopping was observed. This means that online shopping is gradually catching on with the people. It is imperative that online shop companies in Ghana intensify their marketing campaigns to make good use of the positive attitudes developing. Secondly, the study has shown that perceived risk act as barriers for acceptance of online shopping behaviours among the online shoppers. Specifically, perceived financial risk is the greatest barrier to online shopping. This means that marketing strategies that target both actual and potential online shoppers should consider addressing perceived risk directly. There is the need to diffuse the fear by showing in concrete terms steps taken to reduce risk and ensure financial delivery. In this case, the cash-on-delivery strategy could go a long way to addressing the perceived financial risk concerns. When customers know they can pay and goods are delivered and need not necessarily provide their account details online, it will reassure them of the safety and security of their finances and privacy. Again, the findings showed further e-word of mouth reduces the negative effect of perceived risk on online shopping behaviours. This means that when actual online shoppers promote online shopping it reduces the fear and encourages online shopping significantly. Based on this, it is imperative for marketing strategies to include actual online shoppers in marketing campaigns to share their experiences. This would help allay the fears of potential customers and encourage them to shop online through online reviews recommendations from online shoppers’ comments from online shopping sites this will reduce the perceive risk consumers associate with online shopping. 81 The study contributes to the field of consumer behaviour both academia. Particularly in the online shopping and adoption aspect. From academic perspective, this research has responded to important calls that encourage conducting the study on the consumer perceive risk dimension towards online shopper behaviour of consumers. Especially in emerging markets (Aladwani, 2006; Al – Magrabi et al., 2011). The findings are consistent with provision research findings and also help in closing gaps identified previously. This study has made an important contribution by enhancing and extending our understanding the roles played by e- word of mouth towards consumer perceive risk dimension and online shopping behaviours in emerging market such as Ghana. Also, this study represents one of the early attempts by researchers devoted to examine consumer perceive risk on online shopping behaviour in Ghana. Online shop companies can benefit from the findings by designing their electronic marketing strategies and programs to achieve long time. For instance perceive risk dimension has exerted major effect on consumer behaviour towards online shopping. The study also had value to the foreign online companies planning to expand their operation in Ghana. This will help to make the right electronic marketing and managerial decision in order to achieve long term success. More so, this study also contributes to the existing body knowledge by examine consumer perceive risk dimension towards online shopping behaviour. In consumer decision – making context consumer behaviour are significant and relevant (Harssaein & Head 2007) information about literature review it was clear that consumer behaviour are not complete (and most literature focused on purchase and behavioural ( Dai et al., 2014). Another import point is that it add to the examining of consumer perceive risk towards online shopping behaviour in related to the fact that in Ghana online shopping is still at it infancy level and it can be considered as an innovation within this context. 82 Limitation of the study The study although makes a great significant contribution to knowledge, this study carefully points out certain limitations for further research. Firstly, the relationship among consumer perceived risk dimensions and online shopping behaviours are tested using customer online shoppers from a single city Accra, not the entire country Ghana. Those in the other cities might have some characteristics whose were not captured in the study. Therefore, attention need to be taken in generalizing the findings. Again, the study only examined consumer perceived risk dimension, the study did not assess how respondents deal with the perceived risks. The study therefore does not offer understanding into how shoppers in Ghana manage their perceived risk. Finally, the geographical context of the study is Ghana. Although the finding for this research is believed to be applicable to other African countries that show similar characteristics with Ghana in terms of similar experience of electronic commerce in general and online shopping in particular. These findings are not necessarily applicable to other African countries that lagged behind or move beyond Ghana in terms of electronic commerce and online shopping. Therefore, further studies in different African countries would most likely strengthen and validate the findings of the study. Future Research This current study recommend that future studies should be conducted in the other major cities in Ghana to get the whole national view. The study only examine consumer perceived risk dimensions on online shopping behaviour but did not assess how the participants deal 83 with their perceived risk, and also the study therefore does not offer understanding into how online shoppers in Ghana manage their perceived risk. For this it will be better if these could be investigated. In the nutshell, organization adoption of technological innovation could be explore to find out about how firms up take online services and its relationships with consumer adoption of online transactions. 84 REFERENCES Abou-Shouk, M.A., Lim, W.M. and Megicks, P. (2016). Using competing models to evaluate the role of environmental pressures in ecommerce adoption by small and medium sized travel agents in a developing country. Tourism Management, 52, pp.327-339. Agwu, M. E., Atuma, O., Ikpefan, O. 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Life Science Journal, 9(4), 983-987. 100 APPENDICES QUESTIONNAIRE This study is being conducted to examine consumer perceived risk and online shopping in Ghana. As a participant I will therefore indulge on your assistance to respond to the following questions as sincere and honestly as possible. You are encouraged to respond to all questions and your results will be kept with utmost confidentiality. Thank you for your cooperation. PART A: Background of Respondents. Please tick [√] the correct answers from the options provided below. 1. Gender: Male [ ] Female [ ] 2. Marital Status: Single [ ] Married [ ] Divorced [ ] Other [ ] 3. Age: 20 or under [ ] 21-30 [ ] 31-40 [ ] 41-50 [ ] 51-60 [ ] 61+ [ ] 4. Level of education: SHS and below [ ] Diploma [ ] First degree [ ] Masters [ ] Ph.D. [ ] 5 Status: Student[ ] Government worker [ ] Private Organization [ ]Self-employed[ ] Unemployed[ ] 6 How long have you been using shop online? a) Less than a year [ ] b) 1–2 years [ ] c) 3–4 years [ ] d) Above 5 years 101 7 What did you use online shopping for? a) Buying or selling items [ ] b) Searching for product information [ ] c) Money transfer [ ] d) Paying bills [ ] e) Others, please specify PART B: Dimensions of consumer perceived risk in online shopping please, study the statements below and indicate the extent to which you agree or disagree by ticking the appropriate boxes beside them 1-Strongly Disagree, 2-Disagree, 3- Neutral, 4-Agree and 5-Strongly Agree. 1 2 3 4 5 Financial Risk 8 I can’t trust the online company 9 I may not get the product 10 I may purchase something by accident 11 My personal information may not be kept 12 I may not get what I want 13 My credit card number may not be secure 14 I might be overcharged 15 Traditional stores offer more discount than online store. 16 Online stores offer discount price but the total cost is not lower. 102 17 Online payment will charge extra fees. 18 Delivering to the home will charge relatively higher fees. 19 I might lose my money Product Risk 20 The quality of the product is not accepted. 21 The product performance is not consistent with the expectation. 22 The product may be false and the quality will be poor. 23 It is difficult to return when the product is not satisfied. 24 Cannot try on clothing online. 25 Unable to touch and feel the item. 26 Size may be a problem with clothes. 27 Cannot examine the actual products. 28 The product might fail to perform to my satisfaction. 29 Can’t examine the actual product 30 Size may be a problem with clothes 31 Can’t try on clothing online 32 Inability to touch and feel the item 103 33 Must pay for shipping and handling 34 It is difficult for me to judge products' quality adequately. 35 It is difficult for me to compare the quality of similar products. 36 The product purchased may NOT perform as expected. 37 Must wait for merchandise to be delivered Delivering risk 38 Delivered the product to a wrong place. 39 The product is damaged during the delivering. 40 The delivered product could be lost. Process and Time Loss Risk 41 The process of online shopping is complex and inconvenient. 42 To deal with PC for accessing Internet will take too much time. 43 Information transformation is too slow during online shopping. 104 44 I might waste my time or effort getting the product repaired or replaced. Too complicated to place order. Difficult to find appropriate websites Pictures take too long to come up Privacy risk PR1 Shopping on the internet jeopardises my privacy PR2 Internet shopping is more risky than shopping in a store. PR3 My credit card number may not be secure PR4 Worrying about personal address, telephone number could be misused by others. Online retailers may track my shopping habits and history purchases PART C: Online shopping Please indicate the extent to which you agree or disagree to the statements below by ticking the appropriate boxes beside them. 105 Online shopping OS1 I usually shop online OS2 In online individuals get access to information OS3 In online individuals gets wherever they want. OS4 Online saves the effort of visiting stores OS5 Online Items from everywhere are available OS6 Can get good product information online OS7 Online provides broader selection of products. OS8 Access to many brands and retailers in online OS9 OS10 Don’t have to wait to be served in online OS11 No hassles OS12 Not embarrassed if you don’t buy in online OS13 No busy signal in online E-word of mouth Please indicate the extent to which you agree or disagree to the statements below by ticking the appropriate boxes beside them. 106 SI1 Celebrities that endorse products influence my decision to shop online SI2 I think that people who are important to me expect me to shop online SI3 Reputation of the web site influence my purchase decision SI4 I think that my colleagues expect me to shop online SI7 Special offers / sales promotions influence my purchase decision SI9 Peer group (my friends) influence my decision to purchase online SI10 Asking friends and family for advice influence my decision to purchase product online 107