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\title{Risk Perceptions and Online Shopping Intention among Internet Users in Nigeria}
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             \author[1]{Udoka  Otika}

             \author[2]{Olise, Chigbata  Moses}

             \author[3]{Ejiofor, Uloma  Happiness}

             \author[4]{Osuagwu Blessing  Oby}

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\date{\small \em Received: 9 December 2018 Accepted: 2 January 2019 Published: 15 January 2019}

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\begin{abstract}
        


This study was carried out to investigate the influence of risk perception dimensions such as perceived financial risk, perceived performance risk, perceived time risk, perceived psychological risk and perceived social risk on internet users? online shopping intention. The study aims to fill the gap that exists in literature on reasons why Nigerian internet users, who are able to shop online, still refrain from doing so. The study adopted descriptive research design with the aid of survey method in obtaining the needed data. The population comprises all the internet users in the study area. The completed and usable close-ended questionnaire was distributed and collected from 390 respondents within one month. Non-probability sampling procedures (convenience sampling) method was used. The multiple linear regressions were used to test hypotheses with the aid of (SPSS 23 version). The result confirm that perceived time risk has a significant influence on internet users online shopping intention (?=.098, t= 2.392, p<.017). The results also revealed that psychological risk had significant influence on internet users? online shopping intention (?=.163, t= 5.035, p<.000). Performance risk has a significant influence on internet users online shopping intention (?=.101, t= 1.863, p<.063), financial risk also have a significant influence on internet users online shopping intention (?=.619, t=12.488, p<.000).

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\keywords{risk perception, online shopping, shopping intention, internet users.}

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\section[{Introduction a) Background of the Study}]{Introduction a) Background of the Study}\par
Online shopping also known as electronic commerce (e-commerce) is one of the products of advances in technological changes and developments and has changed the way in which business is operated. Online shopping is defined as the process a customer takes to purchase a goods and service over the internet (Jusoh and  {\ref Ling, 2012)}. Online shopping or online retailing is also a form of electronic commerce which allows consumers to directly buy goods or services from a seller over the Internet using a web browser. Alternative names are: e-webstore, e-shop, estore, Internet shop, web-shop, web-store, online store, and virtual store \hyperref[b64]{(Wikipedia, 2013)}. It is the use of the Internet for marketing, identification, payment and delivery of goods and services \hyperref[b4]{(Ayo et. al. 2011}). Some consumers have open heartedly adapted to online shopping while others have fear of uncertainties and of not fulfilling their expectations. These uncertainties are basically perceived risk.\par
The concept of perceived risk was first introduced by \hyperref[b7]{Bauer (1960)} and has been frequently used to address various issues in consumer behaviour.  {\ref Schiffman et. al. (2007)} explains perceived risk as an uncertainty that consumer faces when he cannot foresee the consequences of his purchase decisions. Risks perceived by consumer can become a hurdle in he dynamic nature of business environment has warranted that for business owners to remain in business, they must adapt to the changing environment especially in the area of technology to meet the needs of customers. For example, \hyperref[b65]{Wilson \& Gilligan (1997)} argued that marketers should match the capabilities of the business with the environmental T conditions. Furthermore,  {\ref Pride \& Ferrell (2003)} recommended that businesses should be proactive and adjust their marketing strategies to fit changes taking place in the environment. Again, Lamb, Hair, \& McDaniel (2006) described marketers as adapters rather than agents of change. Consequently, many businesses are responding to the new order in the technology world, the Internet to meet the needs of their dynamic customers. One of these businesses is retailing firm. Like in developed countries, an increasing number of retailers in Nigeria are adopting the Internet as a platform to make sales; this is known as online shopping. The adoption and use of the Internet (a major technological platform) to facilitate socio-economic activities is growing in all parts of the world, and this growth is poised to continue unabated in coming years. Camp L. J.  {\ref (2000)}, aver that internet is a set of networks connected using protocols that are open and portable, and that enable the entire research community to share information. Internet serves several purposes including information search, information sharing, interactive communication, and shopping. performing internet transactions (Gerrard and  {\ref Cunningham, 2003)}. Again \hyperref[b44]{Mitchell (1999)} defined perceived risk as "a subjectively-determined expectation of loss. In the online shopping setting, the level of perceived risk may be magnified due to online consumers' limited physical access to products and sales personnel  {\ref (Park and Stoel, 2005)}. A high level of perceived risk hinders consumers from adopting the Internet as a shopping channel \hyperref[b21]{(Forsythe and Shi, 2003;}\hyperref[b23]{Garbarino and Strahilevitz, 2004)}. Peter and Tarpey, (1975) identified six components of perceived risk associated with online shopping (physical risks, social risks, product risks, convenience risks, financial risks, and psychological risks. Among the six types of risks associated with online shopping, product risks and financial risks have been shown to have a significant negative influence on consumers' Internet purchase intentions \hyperref[b9]{(Bhatnagar and Ghose, 2004;} {\ref Lu, Hsu, and Hsu, 2005)}.\par
Despite the significant growth and the optimistic future growth of online shopping, negative aspects are also becoming more frequently associated with this alternative shopping method. In an online environment, in contrast to a physical one, greater risk and less trust are expected due to the fact that there is major difficulty in evaluating a product or service as there are no visual or tangible indications about the quality of the product nor face-to-face interaction with sales personnel, and the purchase is affected by security and privacy issues \hyperref[b35]{(Laroche et al., 2005)}. Therefore, it is assumed that people may feel a certain degree of risk when purchasing a product through the Internet. For instance, consumers are worried that the Internet still has very little security with respect to using their credit cards and disclosing personal information or concerned about purchasing a product from sellers without physically examining the products \hyperref[b46]{(Pallab, 1996)}. There have been intensive studies on online shopping intentions and behavior in recent years \hyperref[b2]{(Almousa, 2011;} {\ref Thompson and Liu 2011;}\hyperref[b41]{Masoud, 2013;}\hyperref[b18]{Dai, Forsythe and Kwon, 2014)}. Most of them have attempted to identify factors influencing or contributing to online shopping attitudes and behavior. These studies have all made important contributions to our understanding of the dynamics of online shopping field. However, there is a lack of coherent understanding of the influence of perceived risks on online shopping intention in Nigeria. 
\section[{b) Statement of the Problem}]{b) Statement of the Problem}\par
Despite the significant growth and the optimistic future growth of online shopping, negative aspects are also becoming more frequently associated with this alternative shopping method. Therefore, it is assumed that people may feel a certain degree of risk when purchasing a product through the Internet. For example, consumers are worried that the Internet still has very little security with respect to using their credit cards and disclosing personal information or concerned about purchasing a product from sellers without physically examining the products \hyperref[b46]{(Pallab, 1996)}. While businesses in Nigeria are reported to have online access with opportunity for e-commerce activities, customers in the country however access business websites only to source for information but make purchases the traditional way Ayo, Adewoye, and Oni, (2011). In spite of the growing population for online shopping, a large percentage of Internet users find online shopping as a source of risk and uncertainty. As a result of these challenges, not many Nigerian internet users wish to adopt online shopping. For example, in a survey of online shopping behavior of consumers in Nigeria, only 23.3 per cent had ever purchased goods online; 37 per cent had never visited any online shop; 18 per cent had visited 1 to 2 online shops, 24.6 per cent had visited 3 to 5 online shops, 12.6 per cent had visited between 6 to 20 online shops, and 7.8 per cent had visited above 20 online shops \hyperref[b4]{(Ayo, Adewoye, \& Oni, 2011)}. The statistics is obviously very low compared with numbers of consumers who had shopped online in Europe, U.S., Asia/Pacific and South Africa. Furthermore, according to Nigerian Communications Commission (NCC, 2018) Internet users in Nigeria increased marginally to more than 111.6 million in December 2018. If this data is anything to go by, with a population of 180 million, this implies that about 55\% of the Nigerian populations (an average of 6 persons out of every ten) have access to the Internet. The statistics reveals a huge gap between number of internet users and number of online shoppers. There is a need to fill this gap of low patronage of online shopping which could be attributed to risk dimensions. This gap shows the potential of the online market. E-marketers have to focus on this opportunity and try to convert internet users into the online shoppers. 
\section[{c) Objective of the Study}]{c) Objective of the Study}\par
The main objective of the study is to investigate the influence of perceived risk on online shopping intention among internet users in Nigeria. Specifically, the study seeks. 1. To determine the influence of perceived financial risk on online shopping intention among internet users in Nigeria. 2. To establish the influence of perceived psychological risk on online shopping intention among internet users in Nigeria. 3. To examine the influence of perceived performance risk on online shopping intention among internet users in Nigeria. 4. To ascertain the influence of perceived time risk on online shopping intention among internet users in Nigeria.\par
5. To examine the influence of perceived social risk on online shopping intention among internet users in Nigeria. 
\section[{d) Research Hypotheses}]{d) Research Hypotheses}\par
The following hypotheses were formulated based on the research objectives. H0 1 : Perceived financial risk has a negative influence on online shopping intention among internet users in Nigeria.     
\section[{e) Scope of the Study}]{e) Scope of the Study}\par
The study focuses on the influence of perceived risk on online shopping intention among internet users in Nigeria. The data collection and location was from the south eastern states of Nigeria namely, Anambra, Abia, Imo, Enugu and Ebonyi. However, the current study did not include all the internet users in Nigeria. As a result, it is difficult to generalize the results to all the internet users in Nigeria. Numerous factors can affect online shopping intention. This study only evaluated the perceived risk and its dimensions and how it relates to online shopping intention. The independent variables used in the study include; financial risk, psychological risk, performance risk, time risk and social risk. Other types of risk dimensions were not included in the study because they are no relevant to the current study. The study was carried out in six months from March to July, 2019. 
\section[{f) Significance of the Study}]{f) Significance of the Study}\par
This study is very much important since the study will unveil the influence of perceived risk on online shopping intention among internet users in Nigeria. It will be a source of reference to scholars and researchers to carry out further research on the influence of perceived risk on online shopping intention among internet users in Nigeria. In addition, it will be a pedestal for managers and policymakers to make a clear decision on how to convert internet users into online shoppers. The result of the study will enable eretailers to understand and focus on implementing marketing strategies that can lead to the improvement of their business. The study is also very significant as its outcome will spell out the relevance of marketing and the correct marketing practices on e-retailing. Finally, the outcome of the study will be a guide for the regulatory agencies to make policies that will coordinate and promote the activities of e-retailing for survival and growth. 
\section[{II.}]{II.}\par
Literature Review a) Dimensions of Perceived Risk i. Financial Risk Financial risk is defined as the likelihood of a financial loss due to hidden costs or a lack of guarantee in the case of errors ). According to Kiang, Ye, Hao, Chem \& Li, (2011) financial risk is often termed 'economic risk' and is defined as the 'likelihood of suffering a financial loss due to any hidden costs or replacement costs due to the lack of warranty or a faulty product. Price is the product element that has been reported to critically determine a consumer's purchase decision and as the monetary value of a product increases, so does the perceived financial risk associated with the purchase \hyperref[b48]{(Pappas, 2016)}. When using the Internet to purchase products, the fundamental financial risk that consumers perceive, is often said to be related to security and privacy concerns \hyperref[b47]{(Pantano, 2014)}.\par
Privacy and security concerns are important and assist in explaining consumers' resistance to online shopping. Consumers who believe that their online transactions are prone to fraud will be less likely to purchase online \hyperref[b45]{(Nepomuceno et al., 2014)}. Concerns of consumers include the safety of their personal information, the overall transaction security and the misuse of private consumer data. These concerns are fuelled by media headlines on related subjects, such as hacking, fraud and online scams that raise uncertainty about online shopping \hyperref[b14]{(Constantinides, 2004)}. Furthermore, the high concern for security combined with the intangibility of online shopping, increases the perceived financial risk of consumers and decreases the probability that a consumer will shop online.\par
The complexity of new technologies and growing capacity for information processing has made privacy of transactions an increasingly important issue of online shopping \hyperref[b36]{(Lee et al., 2011)}. Many internet users appear to be afraid to shop online or provide personal information online, due to fears of a lack of privacy and the possibility that their information will be misused \hyperref[b62]{(Visa, 2012)}. The expansion of electronic payments provides consumers with the means to participate in the global digital economy and provides retailers with access to a global consumer base. However, to reap the benefits of this new digital economy and increased market, retailers need to understand the perceived risk and safety concerns with regards to online shopping for internet users. Online retailers need to reassure consumers that online © 2019 Global Journals payment methods are safe and that their personal information is secure. Internet users, who perceive less financial risk, will be more likely to shop online. 
\section[{ii. Psychological Risk}]{ii. Psychological Risk}\par
Mitchell and Greatorex (1993) define perceived psychological risk as the potential loss of self-esteem due to a product or service being inconsistent with the self-image of the consumer. Consumers who are riskaverse and more comfortable with traditional shopping methods, will perceive online shopping to be complex and struggle to adopt this new form of retail. Many consumers are not willing to interact with online retailers, which decrease the consumer's intention to shop online \hyperref[b38]{(Lian \& Yen, 2014)}. Such consumers are more comfortable with traditional brick-and-mortar stores and have not made a psychological or 'mental shift' to online shopping.\par
Consumers have previously cited face-to-face contact, interaction with staff and sensory evaluations of a product as reasons for being more willing to use traditional shopping methods versus online shopping \hyperref[b55]{(Samuel, Balaji \& Wei, 2015)}. Not all consumers are conversant with technology and the Internet and therefore, online shopping is often perceived to be complex and difficult to understand and has further been described as impersonal, frustrating and overwhelming by consumers. Perceived psychological risk is increased by the intangible nature of online shopping as consumers purchase a product without having seen or touched it. The lack of sensory product inspection enhances the uncertainty and perceived psychological risk that consumers perceive when shopping online \hyperref[b26]{(Huang et al., 2004)}. As a result, many consumers will search for a product online, but purchase it in-store after having touched and seen the product. In contrast to traditional shopping orientations of some consumers, other consumers enjoy the selfservice function of online shopping. Such consumers are confident, need less support from sales staff and will be more open to shopping online \hyperref[b38]{(Lian \& Yen, 2014)}. Internet users, who are more comfortable with new technology, are expected to enjoy online shopping, in comparison to older consumers who are generally expected to have a higher barrier towards online shopping. 
\section[{iii. Performance Risk}]{iii. Performance Risk}\par
Performance risk is concerned with the potential failure of the product or website to meet expected performance requirements and is formally defined by \hyperref[b44]{Mitchell (1999)} as 'the potential loss occurred by the failure of a product to perform as expected.' According to Jacoby \& Kaplan, (1972) perceived performance risk is the possibility that the product does not work properly or only works for a short period of time and can be applied in the online shopping context to include the performance of a website. As uncertainty about the functionality of the product and website increases, consumers perceive increased performance risk. Because performance risk associated with online shopping involves the performance of the product and the performance of the website, online consumers could perceive higher levels of performance risk than nononline consumers.\par
The first component of performance risk, product risk, is defined as the loss experienced by consumers when their expectations of a product do not actualise after purchase \hyperref[b21]{(Forsythe \& Shi, 2003)}. In the online environment, product risk is largely due to the consumer's inability to physically examine products before purchase or due to limited product information being available. The fact that consumers cannot accurately evaluate the quality of a product prior to purchase, makes product risk an important element of perceived performance risk \hyperref[b25]{(Hsieh \& Tsao, 2014)}. The perceived performance risk of online shopping is further increased by website factors such as time spent searching for information, the uncertainty regarding after sales service and the difficulty of navigation on a website \hyperref[b48]{(Pappas, 2016)}. Website usability includes the ability to find one's way around a website, to locate desired information, to know what to do next and to do so with minimal effort \hyperref[b14]{(Constantinides, 2004)}. The quality of a website is the online equivalent of the atmosphere of a physical store and accordingly acts as a trustworthiness cue for consumers to decrease perceived risk ). In the same way that physical settings of a store affect consumers' psychological and behavioural responses, website atmospheric cues can affect consumers' shopping intentions \hyperref[b53]{(Richard \& Habibi, 2016)}. Shopping in general has been recognised as a recreational activity and despite the high levels of perceived risk, online shopping is no different. 
\section[{iv. Time Risk}]{iv. Time Risk}\par
Within the online context, time risk has been defined as the potential loss of time and effort and includes issues related to website navigation, processing an order and delivery delays (Aghekyan-Simonian, Forsythe, Kwon \& Chattaraman, 2012). According to Mitchell \& Greatorex, (1993) perceived time risk has been defined as the amount of time lost as a result of a product or service failure and time spent correcting the error. This dimension of perceived risk also includes waiting time for the receipt of products, as well as time spent returning incorrect items (Aghekyan-Simonian et al., 2012). Slow, dysfunctional websites (e.g. error messages) and poor interactivity, prompt online consumers to search for alternative shopping channels (e.g. other online websites or brick-and mortar-alternatives), since time saving and convenience are motivations for shopping online \hyperref[b14]{(Constantinides, 2004)}. In short, the perceived time risk associated with online shopping is said to be affected by three factors namely, the website functionality, delivery and information search. The successful functionality of a website reflects the reliability of an online retailer and decreases the time risk perceived by consumers (Goode \& Harris, 2007). To attract new consumers and keep existing consumers, the reliability of a website is vital (i.e. websites must function quickly and without broken links) .\par
In addition to the functionality of a website and the delivery of products being a factor of perceived time risk, the search for information can also increase or decrease time risk perceived by consumers. During the online buying process, information search is a key stage for consumers  {\ref (Vazquez \& Xu, 2009)}. Consumers become more empowered as they search online for the best prices and value for money and accessing information about prices has been considered an important factor in affecting consumers' online shopping intention  {\ref (Vazquez \& Xu, 2009)}. The information search stage is an attempt of consumers to overcome the uncertainty and risk associated with online shopping. The information quality of shopping websites has a considerable impact on the shopping decisions of consumers. The intangibility of online shopping increases the uncertainty experienced by consumers and as a consequence, perceived time risk increases when limited information is provided about a product, resulting in consumers having low self-confidence regarding the purchase evaluation \hyperref[b48]{(Pappas, 2016)}. When shopping online, consumers desire an efficient transfer of information, interaction with others and an abundance of immediate and customised information \hyperref[b25]{(Hsieh \& Tsao, 2014)}. By providing detailed and complete information, retailers can decrease the perceived risk of consumers and reduce uncertainties inherent to the online environment \hyperref[b25]{(Hsieh \& Tsao, 2014)}. A consumer who is more informed about a product, will perceive less time risk when purchasing online \hyperref[b45]{(Nepomuceno et al., 2014)} and high-quality information can satisfy consumers and enhance their confidence in shopping, reducing uncertainties and potential losses. 
\section[{v. Social Risk}]{v. Social Risk}\par
Social risk is defined as the probability that shopping online will result in peers thinking less favourably of the consumer and is often termed 'external psychological risk' . With regards to online shopping, perceived social risk includes subjective norms, which refers to an individual's desire to comply with the expectations of other influential consumers \hyperref[b32]{(Khare et al., 2012)}. Subjective norms capture the consumer's perceptions of the influence of significant others, such as parents, peers and the media \hyperref[b29]{(Javadi et al., 2012)}. Consumers with influential personalities will only shop online if it has been accepted by social circles.\par
Many consumers are influenced by social groups and exhibit a tendency to behave according to social norms, but the extent to which consumers are willing to act on the basis of words of others differs \hyperref[b30]{(Kaur \& Quareshi, 2015)}. Consumers with a strong desire for social recognition are more likely to be influenced by normative influences than consumers with a low desire for social recognition \hyperref[b32]{(Khare et al., 2012)}. Thus, if the social norm has been established to not engage in online shopping or with certain online retailers, some consumers will perceive increased social risk. Group conformity and social norms are important to this cohort as it minimises consumers' perceptions of risk and security \hyperref[b32]{(Khare et al., 2012)}.\par
Consumers trust information received from other consumers and not only does this affect the purchase intention of consumers, but also the reputation of an online retailer. Retailer reputation involves consumers' perceptions of the retailer's image, innovativeness, commitment to satisfaction and product quality  {\ref (Zhang et al., 2011)}. If consumers are influenced to refrain from shopping online in general, or from certain online retailers, their perceived social risk would increase. 
\section[{b) Online Shopping Intention}]{b) Online Shopping Intention}\par
Online shopping has a lot of connotations which are used interchangeably in the extant literature. These are Internet shopping, electronic shopping, and web shopping. Online shopping has been defined by scholars and researchers. On-line shopping is a single, homogenous activity, the selling of goods and services via the World Wide Web (www) \hyperref[b10]{(Birkin, Clarke, \& Clarke, 2002)}. Online shopping is the use of online stores by consumers up until the transactional stage of purchasing and logistics (Monsuwe´, Dellaert, \& Ruyter, 2004). Web shopping is an e-commerce system used by shoppers in the context of business-to-consumer (B2C) or business-to-business (B2B) (Ling, Chai, \& Piew, 2010). These definitions imply that online shopping requires existence of retailers' websites through which shopping is done in a virtual environment devoid of physical contact between sellers and buyers. To attract shoppers to, keep them longer on, and make them return to the sites, e-tailers must design and promote a user-friendly websites. Ultimately, the main goal of online shopping is to provide a platform for shoppers to make exchange of goods and services with retailers.\par
Relevant to the online context, early research by \hyperref[b49]{Pavlou (2003)} found intention to use a website to be an appropriate measure of online purchase intention, when assessing online consumer behaviour. Given that online shopping involves purchasing and information sharing, purchase intention will depend on various factors that need to be enhanced to increase purchase intention amongst online consumers \hyperref[b49]{(Pavlou, 2003)}. The factors that need to be enhanced to increase purchase intention were investigated by  {\ref Chang et al. (2005)}. They categorised the antecedents of online purchase intention into three categories: perceived characteristics of the website, product characteristics and consumer characteristics. In addition to these three categories, prior experience has also been indicated as an antecedent of online purchase intention. Strong online purchase intention often results from consumers who have successful past purchase experience, which aids in reducing uncertainties \hyperref[b37]{(Leeraphong \& Mardjo, 2013)}. Because online shopping is generally perceived to be riskier than traditional shopping, prior purchase experience reduces uncertainty amongst consumers and increases purchase intention \hyperref[b59]{(Thamizhvanan \& Xavier, 2012)}.\par
Previous research has provided evidence for the effect of the dimensions of perceived risk on online purchase intention and behaviour \hyperref[b21]{(Forsythe \& Shi, 2003;}\hyperref[b23]{Garbarino \& Strahilevitz, 2004}), yet little consensus exists for the effect of specific types of perceived risk on online purchase intention (Dai, Forsythe \& Kwon, 2014). For example, \hyperref[b21]{Forsythe and Shi (2003)} argue that perceived privacy risk (included under perceived financial risk in the current study) does not affect online shopping intention, but Doolin, Dillons, \hyperref[b19]{Thompson and Corner (2005)} found that perceived privacy risk often discourages consumers from shopping online. The results of previous studies offer little agreement on the strength of the dimensions of perceived risk on online shopping intention. 
\section[{c) Conceptual Model}]{c) Conceptual Model}\par
Based on the relevant concepts discussed above, a proposed research model is developed to investigate the influence of perceived risks (financial risk, psychological risk, performance risk, time risk, and social risk) on online shopping intention among internet users in Nigeria. 
\section[{d) Theoretical Framework i. Perceived Risk Theory}]{d) Theoretical Framework i. Perceived Risk Theory}\par
Perceived risk is an important factor that is used in various areas of social sciences. Consumer behavior was first examined as risk-taking as well as risk-reducing behavior by Bauer (1960). Bauer's initial proposition was that "consumer behavior involves risk in the sense that any action of a consumer will produce consequences which he/she cannot anticipate with anything approximating certainty, and some of which at least are likely to be unpleasant" (1960). Again, Cox (1967) extended Bauer's seminal conceptualization and developed a two-factor view of risk structure. The amount of perceived risk is construed to be a function of (1) the amount that would be lost (i.e., that which is at stake) if the consequences of the act were not favorable, and (2) the individual's subjective feeling of certainty that the consequences will be unfavorable"  {\ref (Cox, 1967)}. Similarly,  {\ref Cunningham (1967)} proposed two basic components for perceived risk, which are uncertainty and consequences. Uncertainty refers to the likelihood of unfavorable outcomes, and consequences refer to the importance of losses. Consequences are also divided into two categories as performance and psychosocial consequences.  {\ref Bettman (1972}\hyperref[b8]{Bettman ( , 1973}) used a different distinction for risk as "inherent risk" and "handled risk". He defined inherent risk as the latent risk that a product class holds for a consumer, and handled risk as the amount of conflict a product or product class causes when the purchaser chooses a brand in a particular buying situation. Handled risk includes the effects of information, risk reduction processes, and the degree of risk reduction provided by familiar buying situations \hyperref[b8]{(Bettman, 1973;} {\ref Ross, 1975}). An important study was conducted by \hyperref[b27]{Jacoby and Kaplan (1972)} to determine the interrelationships among the five types of consequences (financial risk, performance risk, physical risk, social risk and psychological risk) and to measure their individual and collective relationship to overall perceived risk. The results indicated that the five types of consequences explained an average of 74\% of the variance in the overall perceived risk measures taken across 12 different products. Performance risk tended to explain more variance than did any other type of consequence. A cross validation of components of perceived risk was later conducted by Kaplan et al.  {\ref (1974)} and the results reflected high agreement with the Jacoby and Kaplan's (1972) findings. Hence, the study indicated that overall perceived risk can be fairly well predicted with the five types of consequences. Later, the risk literature suggested different types of consequences, or dimensions, such as time risk (e.g. \hyperref[b54]{Roselius, 1971;}\hyperref[b57]{Stone and Gronhaug, 1993)}, and privacy risk (e.g. \hyperref[b20]{Featherman and Pavlou, 2003)}.\par
This study argues that understanding to what extent each type of risk dimension contributes to overall risk is critical to reduce risk perceptions during the innovation adoption process of consumers. Especially, really new products/services are innovative and require consumer learning. They are therefore associated with various risks. Hence, five dimensions of perceived risk, including financial risk, performance risk, social risk, psychological risk, and time risk are used in this research as the potential factors that influence internet user's intention to shop of online. 
\section[{e) Empirical Review}]{e) Empirical Review}\par
Folarin and Ogundare (2016) in their influence of customers' perceived risk on online shopping intention in Malaysia's Apparel Industry. The main purpose of the research was to examine the influence of customers' perceived risk on online shopping intention in Malaysia, specifically on Malaysia's apparel industry. To achieve the purpose, the research deduced its conceptual framework from past researches, using five independent variables which are; information privacy risk, security risk, delivery risk, financial risk and quality risk with online shopping intention as the only dependent variable. The conceptualized framework was used to develop a multivariate likert-scale questionnaire with a scale of 1 to 5 and the questionnaire was used to conduct a primary research which solicited responses from 307 customers' who still prefer to shop for apparel in bricks and mortar than shopping online. Thus, convenience sampling which is a type of non-probability sampling that enables easy selection of the target audience was employed (Saunders,  {\ref Lewis and Thornhill, 2009)}. The data collected was quantitatively analyzed via SPSS 20.0, the bivariate multiple regression result shows information privacy risk, financial risk and quality risk have significant influence on online shopping intention in Malaysia's apparel industry, however, security risk and delivery does not have significant influence on online shopping intention in Malaysia's apparel industry. Conclusively, this research had been able to help businesses to identify the customers' perceived risk that inhibit Malaysia customers' from shopping online and it gave recommendations on how businesses can minimize these risks.  {\ref Masoud (2015)} studied the Effect of Perceived Risk on Online Shopping in Jordan, the study aimed to examine the effect of perceived risks (financial risk, product risk, time risk, delivery risk, and information security risk) on online shopping behavior in Jordan. To investigate the hypotheses of the research, data was collected from online shopping users; a survey was conducted with a sample size of 395 online shoppers among consumers who previously purchased online and mainly from the main popular online stores in Jordan, methodology was done using SPSS 17 and Amos 18. The study revealed that financial risk, product risk, delivery risk, and information security risk negatively affect online shopping behavior. The results also showed that the other two dimensions, perceived time risk, and perceived social risk have no effect on online shopping. The study has an important managerial implication; it provides marketers with the importance of consumers risk perception in order to adopt adequate risk-reduction strategies in the internet shopping environment.\par
Zhang et al. (  {\ref 2012}) aimed to explore the dimensions of consumers' perceived risk, and investigate their influence on online consumers' purchasing behavior. The results showed that there are five independent dimensions, perceived health risk, perceived quality risk, perceived time risk, perceived delivery risk and perceived after-sale risk affect significantly online consumers' purchasing behavior. While the other three dimensions, perceived privacy risk, perceived social risk and perceived economic risk are the less relevant factors. \hyperref[b29]{Javadi et al. (2012)} aimed to analyze factors affecting on online shopping behavior of consumers, and how perceived risks (financial risks, product risk, convenience risk and non-delivery risk) impact attitude toward online shopping. To investigate the hypotheses of the research, 200 questionnaires dispersed among online stores of Iran. Respondents to the questionnaire were consumers of online stores in Iran which randomly selected. The study identified that financial risks and non-delivery risk negatively affected attitude toward online shopping, and no significant effect of product risks and convenience risk on attitude toward online shopping. Results also indicated that domain specific innovativeness and subjective norms positively affect online shopping behavior. Furthermore, attitude toward online shopping positively affected online shopping behavior of consumers.\par
Almousa (2011) aimed to examine the influence of six perceived risk dimensions including, performance, psychological, financial, social, time, and privacy risks, associated with apparel online shopping on purchase intention among Saudi consumers. Results indicated that risk perception has a strong negative influence on apparel purchase intention. Nevertheless, differences are observed between different risk dimensions, where consumers perceive more performance and time risks in apparel internet shopping. Moreover, consumers perceive privacy and social risks with a lesser significance than performance and time risks on apparel internet shopping. 
\section[{III.}]{III.} 
\section[{Methodology}]{Methodology}\par
The study adopted descriptive research design with the aid of survey method in obtaining the needed data. According to Cohen, Manion, and Morrison, 2008), survey is useful in that it usually: represents a wide target population, generates numerical data, provides descriptive, inferential and explanatory information, manipulates key factors and variables to derive frequencies, gathers standardized information (i.e. using the same instruments and questions for all participants), and others. The primary source of data was generated through the administration of the questionnaire on the respondents who are the internet users in south eastern state of Nigeria. Secondary data was collected from review of publications, books, internet, unpublished materials (theses and dissertation), and journals. The population of the study comprises all the internet users in the study area. The completed and usable closeended questionnaire was distributed and collected from 390 of 400 respondents within one month. It used nonprobability sampling procedures via a convenience sampling method. The questionnaire was adapted from a mixture of instruments for measuring various aspects of the study. The items, were measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5. To 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XIX Issue VI Version I Year 2019 ( ) ensure the reliability of the instrument, test-retest method of reliability was applied, using Pearson product moment correlation coefficient as the statistical tool, the result gave reliability index of (0.96) indicating a high degree of consistency. The questionnaire was personally administered, through the use of resource persons, to the chosen respondents. The study applied descriptive statistics as a tool to analyze bio-data of the respondents and the research questions, factor analysis, and multiple linear regressions were used to test hypotheses. All analyses were done through the application of Statistical Package for Social Science (SPSS 23 version).\par
IV. 
\section[{Data Analysis}]{Data Analysis}\par
The total number of four hundred (400) copies of the questionnaire was administered by the researcher through handy. Out of the 400 copies of the questionnaire sent out three hundred and ninety were returned, giving a response rate of 97.5\%; 10 out of the 400 copies of the questionnaire administered were not returned, thus, giving a non-response rate of 2.5\%.\par
Demographic profile of respondents. Of total respondents, 53.6\% are female while the remaining 46.4\% participants were male; the response indicates the age bracket of 20 t0 30 years 42.1\% representing youthful age; 51.8\% of the respondents had higher level of education; and half of the respondents spent six to nine hours on the internet daily. 
\section[{a) Descriptive statistics}]{a) Descriptive statistics}\par
Descriptive statistics analysis show that majority of the variables have mean above 3 which show positive response and agreement with the dimensions of the research model. Many of the items, however, have standard deviations above one which is an indication of variation in the opinions of the respondents.\par
Factor Analysis was used to check the loadings of the various items. The result of the Factor analysis shows that Kaiser-Meyer-Olkin Measure of Sampling Adequacy is .504 which is slightly above the .5 benchmark and this confirms the adequacy of the sample used in the study. Also, Bartlett's Test of Sphericity has an approximate Chi-Square value of 13180.061 and is highly statistically significant at .000 above the .01 margin of error. This means the Factor Analysis is reliable and dependable. Total variance explained is 70.175\% which is quite appreciable. For the factor loadings (commonalities), all the items loaded are very high above the .4 benchmark which is an indication that none of the items need be eliminated in the final analysis. The next stage of the analysis is the multiple linear regression (MLR) analysis and the results are shown below. The first information from the MLR analysis is the model summary and from this, the coefficient of multiple correlation R is .879. The coefficient of multiple determination R 2 is .773 while the adjusted R 2 which adjusts the R 2 downwards taking care of error is .770. This means that between 77\% and 77.3\% of variations in the dependent variable, Shopping Intention is accounted for by the five independent variables. 
\section[{Model}]{Model}\par
Sum of Squares Df Mean Square F Sig. The next information from the MLR is the regression analysis of variance ANOVA which has a value of 262.198 and is highly statistically significant at .000 below the .01 margin of error. This means that the model was a good fit and that the coefficient of multiple correlations R is significantly different from zero. The next information is the coefficients. The coefficients show that 4 out of the 5 variables are significant and the hypotheses should be accepted in the alternate form. Social risk is not significant hence the hypothesis on that should be accepted in null form. Tolerance and variance inflation factor (VIF) measure collinearity. For Tolerance, the closer to .1 the below while for VIF 5 is the threshold. The values of both the tolerance and VIF are within acceptable range as they did not show any problem of collinearity. This implies that online shopping intention among internet users is hampered by financial risk, psychological risk, performance risk, time risk, and social risk. 
\section[{b) Discussion of Results}]{b) Discussion of Results}\par
This study examined the effect of perceived risk factors (performance risk, financial risk, time risk, psychological risk and social risk) on internet users' online shopping intention. The results through the multiple regression analytical tool discovered that the influence of perceived risk and its significances were divergent depending on the dimensions of the perceived risk.\par
Internet users consider perceived financial risk as the most important factor contributing to their reluctance in shopping online -more so than the other components of perceived risk, such as performance risk, time risk, psychological risk and social risk. This finding is in agreement with previous studies that found that perceived financial risk was consistently determined to be the most significant predictor of online shopping behaviour  {\ref (} Perceived performance risk was also found to be significant on online shopping intention; this may be as a result of internet users' skeptic about the quality of the product and the website to meet expected performance requirements. As uncertainty about the functionality of the product and website increases, consumers perceive increased performance risk. Because performance risk associated with online shopping involves the performance of the product and the performance of the website, online shoppers perceive higher levels of performance risk. The finding The result also discovered that perceived social risk had no significant effect on the online shopping intention. This is because many internet users do not wish to be influenced by social groups; they intend to act on their own. Previous findings indicate that internet shoppers did not perceive a higher level of social risk. (Chang, \& Tseng, 2013; Hong \& Cha, 2013; Kukar-Kinney \& Close, 2010). However, Ko, Jung, Kim and Shim (2004) found that Korean internet users perceived higher levels of social risk.\par
Furthermore, perceived time risk was found to have significant influence on online shopping intention, this is because internet users fill that much time is wasted during ordering, receiving, and returning an unsatisfactory product. This finding is in line with \hyperref[b1]{Akram (2008)} and \hyperref[b61]{Tian and Ren (2009)} who show that consumers are concerned about the delay and time loss involved in online shopping. \hyperref[b21]{Forsythe and Shi (2003)} found that some online shoppers may hesitate to buy through the internet due to concerns about inconvenience or delays in receiving products.\par
The result also show that perceived psychological risks have significant influence on online shopping intention, which may be due to internet users filling loss of self-esteem due to a product or service being inconsistent with the self-image of the consumer. Therefore, psychological risk was significant enough to negatively affect consumer's online shopping intention. This result is in line with Akram (2008), Lian \& Yen,( 2014)'s findings, but contradicts that of Hong and Cha (2013).\par
V. 
\section[{Summary, Conclusion and Recommendation a) Summary of Findings}]{Summary, Conclusion and Recommendation a) Summary of Findings}\par
The results of multiple linear regression analysis shows that,\par
2) The results also revealed that psychological risk had significant influence on internet users online shopping intention (?=.163, t= 5.035, p<.000). 3) Performance risk has a significant influence on internet users online shopping intention (?=.101, t= 1.863, p<.063), 4) Financial risk has a significant influence on internet users online shopping intention (?=.619, t= 12.488, p<.000). 5) Social risk has no significant influence on internet users online shopping intention (?=.023, t= .631, p=.528). 
\section[{b) Conclusions}]{b) Conclusions}\par
The emergence of internet has impacted the world's marketing environment in a great way and it has provided businesses the opportunity to expand and enhance their ability to reach customers in different locations, both locally and globally through electronic commerce \hyperref[b41]{(Masoud, 2013)}. However, despite this popularity of online shopping and the myriads of benefits that comes with it, many customers still avert online shopping because of the risk associated with it, as there had been issues of privacy and security breach, credit-card fraud, non-delivery, lack of guarantee of quality goods and services among customers'  {\ref (Rao, 2012;} {\ref Meskaran et al, 2013)}. This risk associated with online shopping, brought about the essence of this study which had endeavor to research the reasons behind the aversion of online shopping among internet users in Nigeria.\par
Hence, the variables used for the study were deduced and conceptualized from prior researches on the influence of perceived risk on online intention; these variables include financial risk, performance risk, psychological risk, time risk and social risk \hyperref[b41]{(Masoud, 2013;}\hyperref[b18]{Dai, Forsythe and Kwon, 2014)}.\par
The study applied descriptive statistics as a tool to analyze bio-data of the respondents and the research questions, factor analysis, and multiple linear regressions were used to test hypotheses. The finding revealed that risk, performance risk, psychological and time risk have significant influence on online shopping intention in Nigeria. 
\section[{c) Recommendation}]{c) Recommendation}\par
Online marketers should assure internet users that their money, information on the credit card are secured, this will make them wish to shop online.\par
Again, online marketers should make ordering and delivery of the product seamless to avoid unnecessary time lost.\par
Through advertising, online marketers should reassure internet users of the quality of products displayed for sale.\par
Having identified the risk perceptions of the internet users, it is pertinent for online marketers to develop and improve their reliability and believability in order to gain consumer confidence in shopping online.\par
Furthermore, online marketers should take several actions to improve internet marketing in order to meet customers' needs and expectations.\par
Policy makers or government should provide better regulations to protect consumer's rights, specifically pertaining to online transactions. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XIX Issue VI Version I Year 2019 ( )       			\footnote{© 2019 Global Journals} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[ Canadian Social Science]{b3}\label{b3} 	 		\textit{},  	 	 		\textit{Canadian Social Science}  		7  (2)  p. .  	 
\bibitem[ Prospects and challenges. African Journal of Business Management]{b5}\label{b5} 	 		\textit{},  	 	 		\textit{Prospects and challenges. African Journal of Business Management}  		5  (13)  p. .  	 
\bibitem[ Asia Pacific Journal of Marketing and Logistics]{b31}\label{b31} 	 		\textit{},  	 	 		\textit{Asia Pacific Journal of Marketing and Logistics}  		27  (5)  p. .  	 
\bibitem[ Boston Graduate School of Business Administration ()]{b17}\label{b17} 	 		\textit{},  	 	 		\textit{Boston Graduate School of Business Administration}  		1967. Harvard University Press. p. .  	 
\bibitem[Ayo et al. ()]{b4}\label{b4} 	 		\textit{},  		 			C K Ayo 		,  		 			J O Adewoye 		,  		 			A A Oni 		.  		2011.  	 
\bibitem[Kiang et al. ()]{b33}\label{b33} 	 		‘A service-orientated analysis of online product classification methods’.  		 			M Y Kiang 		,  		 			Q Ye 		,  		 			Y Hao 		,  		 			M Chem 		,  		 			Y Li 		.  	 	 		\textit{Journal of Decision Support Systems}  		2011. 52  (1)  p. .  	 
\bibitem[Richard and Habibi ()]{b53}\label{b53} 	 		‘Advanced modelling of online consumer behaviour: the moderating roles of hedonism and culture’.  		 			M O Richard 		,  		 			M R Habibi 		.  	 	 		\textit{Journal of Business Research}  		2016. 69  (3)  p. .  	 
\bibitem[Javadi et al. ()]{b29}\label{b29} 	 		‘An analysis of factors affecting on online shopping behaviour of consumers’.  		 			M H M Javadi 		,  		 			R H Dolatabadi 		,  		 			M Nourbakhsh 		,  		 			A Poursaeedi 		,  		 			A Asadollahi 		,  		 			A Reza 		.  	 	 		\textit{International Journal of Marketing Studies}  		2012. 4  (5)  p. .  	 
\bibitem[Samuel et al. ()]{b55}\label{b55} 	 		‘An investigation of online shopping experience on trust and behavioural intentions’.  		 			L H S Samuel 		,  		 			M S Balaji 		,  		 			K K Wei 		.  	 	 		\textit{Journal of Internet Commerce}  		2015. 14  (2)  p. .  	 
\bibitem[Lee et al. ()]{b36}\label{b36} 	 		‘Analysing key determinants of online repurchase intentions’.  		 			C H Lee 		,  		 			U C Eze 		,  		 			N O Ndubisi 		.  	 	 		\textit{Asia Pacific Journal of Marketing and Logistics}  		2011. 23  (2)  p. .  	 
\bibitem[Teo and Yeong ()]{b58}\label{b58} 	 		‘Assessing the consumer decision process in the digital marketplace’.  		 			T S H Teo 		,  		 			Y D Yeong 		.  	 	 		\textit{The International Journal of Management Science}  		2003. 31 p. .  	 
\bibitem[Thompson and Teo ()]{b60}\label{b60} 	 		‘Attitude towards online shopping and the Internet’.  		 			S H Thompson 		,  		 			Teo 		.  	 	 		\textit{Behavior and Information Technology},  				2002. 21 p. .  	 
\bibitem[Khare et al. ()]{b32}\label{b32} 	 		‘Attracting shoppers to shop online-challenges and opportunities for the Indian retail sector’.  		 			A Khare 		,  		 			A Khare 		,  		 			S Singh 		.  	 	 		\textit{Journal of Internet Commerce}  		2012. 11  (2)  p. .  	 
\bibitem[Available (2016)]{b63}\label{b63} 	 		 \url{http://www.visa-asia.com/ap/sea/mediacenter/pressrelease/includes/uploads/Visa\textunderscore Gen\textunderscore Y\textunderscore Report\textunderscore 2012\textunderscore LR.pdf}  		\textit{Available},  				2016. September 27.  	 
\bibitem[Birkin et al. ()]{b10}\label{b10} 	 		 			M Birkin 		,  		 			G Clarke 		,  		 			M Clarke 		.  		\textit{Retail geography and intelligent network planning},  				2002. Chictster: Wiley.  	 
\bibitem[Bauer ()]{b6}\label{b6} 	 		‘Conceptualization and measurement of perceived risk in online shopping’.  		 			R A Bauer 		.  	 	 		\textit{Marketing Management Journal}  		Hassan A. M. , Kunz M.B., Pearson A. W. and Mohamed F A (ed.)  		1967. 2006. 16  (1)  p. .  	 
\bibitem[Visa ()]{b62}\label{b62} 	 		\textit{Connecting with Millennials},  		 			Visa 		.  		2012.  	 
\bibitem[Pavlou ()]{b49}\label{b49} 	 		‘Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model’.  		 			P A Pavlou 		.  	 	 		\textit{International Journal of Electronic Commerce}  		2003. 7  (3)  p. .  	 
\bibitem[Schiffman et al. ()]{b56}\label{b56} 	 		\textit{Consumer Behaviour},  		 			L G Schiffman 		,  		 			L L Kanuk 		,  		 			J Wisenblit 		.  		2010. New Jersey: Pearson Prentice Hall.  	 
\bibitem[Bauer ()]{b7}\label{b7} 	 		\textit{Consumer behaviour as risktaking. Dynamic marketing for a changing world},  		 			R A Bauer 		.  		1960. Chicago: American Marketing Association.  	 
\bibitem[Forsythe and Shi ()]{b21}\label{b21} 	 		‘Consumer patronage and risk perceptions in Internet shopping’.  		 			S Forsythe 		,  		 			B Shi 		.  	 	 		\textit{Journal of Business Research}  		2003. 56  (11)  p. .  	 
\bibitem[Mitchell ()]{b44}\label{b44} 	 		‘Consumer perceived risk: Conceptualisations and models’.  		 			V W Mitchell 		.  	 	 		\textit{European Journal of Marketing}  		1999. 33  (1)  p. .  	 
\bibitem[Chang and Wang ()]{b11}\label{b11} 	 		‘Consumer preferences for service recovery options after delivery delay with shopping online’.  		 			D S Chang 		,  		 			T H Wang 		.  	 	 		\textit{Social Behaviour and Personality: An International Journal}  		2012. 40  (6)  p. .  	 
\bibitem[Roselius ()]{b54}\label{b54} 	 		‘Consumer rankings of risk reduction methods’.  		 			E Roselius 		.  	 	 		\textit{Journal of Marketing}  		1971. 35  (1)  p. .  	 
\bibitem[Jacoby ()]{b28}\label{b28} 	 		‘Consumer research: a state of the art review’.  		 			J Jacoby 		.  	 	 		\textit{Journal of Marketing}  		1978. 42  (2)  p. .  	 
\bibitem[Ko et al. ()]{b34}\label{b34} 	 		‘Cross-cultural differences in perceived risk of online shopping’.  		 			H Ko 		,  		 			J Jung 		,  		 			J Y Kim 		,  		 			S W Shim 		.  	 	 		\textit{Journal of Interactive Advertising}  		2004. 4  (2)  p. .  	 
\bibitem[Thamizhvanan and Xavier ()]{b59}\label{b59} 	 		‘Determinants of consumers' online purchase intention: an empirical study in India’.  		 			A Thamizhvanan 		,  		 			M J Xavier 		.  	 	 		\textit{Journal of Indian Business Research}  		2012. 5  (1)  p. .  	 
\bibitem[Forsythe et al. ()]{b22}\label{b22} 	 		‘Development of a scale to measure the perceived benefits and risks of online shopping’.  		 			S Forsythe 		,  		 			C L Liu 		,  		 			D Shannon 		,  		 			L C Gardner 		.  	 	 		\textit{Journal of Interactive Marketing}  		2006. 20  (2)  p. .  	 
\bibitem[Zhang et al. ()]{b40}\label{b40} 	 		\textit{Dimensions of Consumers' Perceived Risk and Their Influences on Online Consumers' Purchasing Behavior},  		 			Lingying Zhang 		,  		 			Wojie Tan 		,  		 			Yingcong Xu \& Genlue Tan 		.  		 \url{www.jcisme.org?C2011-2012WorldAcademicPublishing}  		2012. 2 p. .  	 
\bibitem[Huang et al. ()]{b26}\label{b26} 	 		‘Effect of brand name on consumers'' risk perceptions of online shopping’.  		 			W Huang 		,  		 			H Schrank 		,  		 			A J Dubinsky 		.  	 	 		\textit{Journal of Consumer Behaviour}  		2004. 4  (1)  p. .  	 
\bibitem[Kaur and Quareshi ()]{b30}\label{b30} 	 		\textit{Factors obstructing intentions to trust and purchase products online},  		 			G Kaur 		,  		 			T K Quareshi 		.  		2015.  	 
\bibitem[Garbarino and Strahilevitz ()]{b23}\label{b23} 	 		‘Gender differences in the perceived role of buying online and the effects of receiving a site recommendation’.  		 			E Garbarino 		,  		 			M Strahilevitz 		.  	 	 		\textit{Journal of Business Research}  		2004. 57  (7)  p. .  	 
\bibitem[Akram ()]{b1}\label{b1} 	 		\textit{How perceived risk affects online purchase intention: Consumer's perspective. Unpublished doctoral dissertation},  		 			M S Akram 		.  		2008. France.  		 			Universite Paul Cézanne 		 	 
\bibitem[Nepomuceno et al. ()]{b45}\label{b45} 	 		‘How to reduce perceived risk when buying online: The interactions between intangibility, product knowledge, brand familiarity, privacy and security concerns’.  		 			M V Nepomuceno 		,  		 			M Laroche 		,  		 			M Richard 		.  	 	 		\textit{Journal of Retailing and Consumer Services}  		2014. 21  (4)  p. .  	 
\bibitem[Pires et al. ()]{b52}\label{b52} 	 		‘Influence on the Perceived Risk of purchasing online’.  		 			G Pires 		,  		 			J Stanton 		,  		 			A Eckford 		.  	 	 		\textit{Journal of Consumer Behavior}  		2004. 4  (2)  p. .  	 
\bibitem[Pires et al. ()]{b51}\label{b51} 	 		‘Influences on the perceived risk of purchasing online’.  		 			G Pires 		,  		 			J Stanton 		,  		 			A Eckford 		.  	 	 		\textit{Journal of Consumer Behaviour}  		2004. 4  (2)  p. .  	 
\bibitem[Constantinides ()]{b14}\label{b14} 	 		‘Influencing the online consumer's behaviour: the web experience’.  		 			E Constantinides 		.  	 	 		\textit{Journal of Internet Research}  		2004. 14  (2)  p. .  	 
\bibitem[Pantano ()]{b47}\label{b47} 	 		‘Innovation drivers in retail industry’.  		 			E Pantano 		.  	 	 		\textit{International Journal of Information Management}  		2014. 34  (3)  p. .  	 
\bibitem[Laroche et al. ()]{b35}\label{b35} 	 		‘Internet versus bricks-and-mortar retailers: an investigation into intangibility and its consequences’.  		 			M Laroche 		,  		 			Z Yang 		,  		 			G Mcdougall 		,  		 			J Bergeron 		.  	 	 		\textit{Journal of Retailing}  		2005. 81  (4)  p. .  	 
\bibitem[Pallab ()]{b46}\label{b46} 	 		‘Marketing on the Internet’.  		 			P Pallab 		.  	 	 		\textit{Journal of Consumer Marketing}  		1996. 13  (4)  p. .  	 
\bibitem[Pappas ()]{b48}\label{b48} 	 		‘Marketing strategies, perceived risks, and consumer trust in online buying behaviour’.  		 			N Pappas 		.  	 	 		\textit{Journal of Retailing and Consumer Services}  		2016. 29  (1)  p. .  	 
\bibitem[Wikipedia ()]{b64}\label{b64} 	 		‘Online shopping’.  		 			Wikipedia 		.  	 	 		\textit{Wikipediaonline Encyclopaedia},  				2013.  	 
\bibitem[Lian and Yen ()]{b38}\label{b38} 	 		‘Online shopping drivers and barriers for older adults: age and gender differences’.  		 			J Lian 		,  		 			D C Yen 		.  	 	 		\textit{Journal of Computers in Human Behaviour}  		2014. 2014. 37 p. .  	 
\bibitem[Cox and Rich ()]{b15}\label{b15} 	 		‘Perceived risk and consumer decision-making -the case of telephone shopping’.  		 			D F Cox 		,  		 			S U Rich 		.  	 	 		\textit{Journal of Marketing Research}  		1964. 1  (4)  p. .  	 
\bibitem[Bettman ()]{b8}\label{b8} 	 		‘Perceived risk and its components’.  		 			J R Bettman 		.  	 	 		\textit{Journal of Consumer Research}  		1973. 10 p. .  	 
\bibitem[Almousa ()]{b2}\label{b2} 	 		\textit{Perceived risk in apparel online shopping: A Multi-dimensional perspective},  		 			M Almousa 		.  		2011.  	 
\bibitem[Doolin et al. ()]{b19}\label{b19} 	 		‘Perceived risk, the Internet shopping experience and online purchasing behaviour: A New Zealand Perspective’.  		 			B Doolin 		,  		 			S Dillons 		,  		 			F Thompson 		,  		 			J L Corner 		.  	 	 		\textit{Journal of Global Information Management}  		2005. 2005. 13  (2)  p. .  	 
\bibitem[Stone and Gronhaug ()]{b57}\label{b57} 	 		‘Perceived risk: Further considerations for the marketing discipline’.  		 			R N Stone 		,  		 			K Gronhaug 		.  	 	 		\textit{European Journal of Marketing}  		1993. 27  (3)  p. .  	 
\bibitem[Featherman and Pavlou ()]{b20}\label{b20} 	 		‘Predicting e-services adoption: A perceived risk facets perspective’.  		 			M S Featherman 		,  		 			P A Pavlou 		.  	 	 		\textit{International Journal of Human-Computer Studies}  		2003. 59  (4)  p. .  	 
\bibitem[Hsieh and Tsao ()]{b25}\label{b25} 	 		‘Reducing perceived online shopping risk to enhance loyalty: website quality perspective’.  		 			M Hsieh 		,  		 			W Tsao 		.  	 	 		\textit{Journal of Risk Research}  		2014. 17  (2)  p. .  	 
\bibitem[Zhang et al. ()]{b66}\label{b66} 	 		‘Repurchase intention in B2C e-commerce-a relationship quality perspective’.  		 			Y Zhang 		,  		 			Y Fang 		,  		 			K Wei 		,  		 			E Ramsey 		,  		 			P Mccole 		,  		 			H Chen 		.  	 	 		\textit{Journal of Information and Management}  		2012. 48  (6)  p. .  	 
\bibitem[Chang and Tseng ()]{b12}\label{b12} 	 		‘Research note: E-store image, perceived value and perceived risk’.  		 			E C Chang 		,  		 			Y F Tseng 		.  	 	 		\textit{Journal of Business Research}  		2013. 2013. 66 p. .  	 
\bibitem[Tian and Ren ()]{b61}\label{b61} 	 		\textit{Research on the perceived risk of consumer in e-business. Unpublished doctoral dissertation},  		 			L Tian 		,  		 			G Ren 		.  		2009. Beijing, China.  		 			Beijing Union University 		 	 
\bibitem[Mitchell and Greatorex ()]{b43}\label{b43} 	 		‘Risk perception and reduction in the purchase of consumer services’.  		 			V Mitchell 		,  		 			M Greatorex 		.  	 	 		\textit{The Service Industries Journal}  		1993. 13  (4)  p. .  	 
\bibitem[Bhatnagar and Ghose ()]{b9}\label{b9} 	 		‘Segmenting consumers based on the benefits and risks of Internet shopping’.  		 			A Bhatnagar 		,  		 			S Ghose 		.  	 	 		\textit{Journal of Business Research}  		2004. 57 p. .  	 
\bibitem[Wilson and Gilligan ()]{b65}\label{b65} 	 		\textit{Strategic marketing management, planning, mimplementation and control},  		 			R M S Wilson 		,  		 			C Gilligan 		.  		1997. Oxford: Butterworth-Heinemann.  	 	 (2nd ed) 
\bibitem[Jacoby and Kaplan ()]{b27}\label{b27} 	 		‘The components of perceived risk’.  		 			J Jacoby 		,  		 			L B Kaplan 		.  	 	 		\textit{Third Annual Conference of the Association for Consumer Research},  				 (Chicago, Illinois)  		1972. p. .  	 
\bibitem[Masoud ()]{b41}\label{b41} 	 		‘The Effect of Perceived Risk on Online\textbackslash Shopping in Jordan’.  		 			Y E Masoud 		.  	 	 		\textit{European Journal of Business and Management}  		2013. 5  (6)  p. .  	 
\bibitem[Ling et al. ()]{b39}\label{b39} 	 		‘The effects of shopping orientations, online trust and prior online purchase experience toward customers' online purchase intention’.  		 			K C Ling 		,  		 			L T Chai 		,  		 			T H Piew 		.  	 	 		\textit{International Business Research}  		2010. 3  (3)  p. .  	 
\bibitem[Persad and Padayachee ()]{b50}\label{b50} 	 		‘The factors that influence customer e-services adoption’.  		 			K Persad 		,  		 			K Padayachee 		.  	 	 		\textit{South African Consumer Journal}  		2015. 56 p. .  	 
\bibitem[Dai et al. ()]{b18}\label{b18} 	 		‘The impact of online shopping experience on risk perceptions and online purchase intentions: does product category matter’.  		 			B Dai 		,  		 			S Forsythe 		,  		 			W Kwon 		.  	 	 		\textit{Journal of Electronic Commerce Research}  		2014. 15  (1)  p. .  	 
\bibitem[Chang and Chen ()]{b13}\label{b13} 	 		‘The impact of online store environment cues on purchase intention, trust and perceived risk as mediator’.  		 			H H Chang 		,  		 			S W Chen 		.  	 	 		\textit{Online Information Review}  		2008. 32  (6)  p. .  	 
\bibitem[Chang and Chen ()]{b24}\label{b24} 	 		‘The impact of online store environment cues on purchase intentions: Trust and Perceived Risk as a mediator’.  		 			H H Chang 		,  		 			S W Chen 		.  	 	 		\textit{Online Information Review}  		2008. 32  (6)  p. .  	 
\bibitem[Cunningham]{b16}\label{b16} 	 		‘The major dimensions of perceived risk’.  		 			S M Cunningham 		.  	 	 		\textit{Risk taking and information handling in consumer behavior},  				 			D Cox 		 (ed.)  		 	 
\bibitem[Aghekyan-Simonian et al. ()]{b0}\label{b0} 	 		‘The role of product brand image and online store image on perceived risks and online purchase intentions for apparel’.  		 			M Aghekyan-Simonian 		,  		 			S Forsythe 		,  		 			W S Kwon 		,  		 			V Chattaraman 		.  	 	 		\textit{Journal of Retailing and Consumer Services}  		2012. 19  (3)  p. .  	 
\bibitem[Leeraphong and Mardjo ()]{b37}\label{b37} 	 		‘Trust and risk in purchase intention through online social network: a focus group study of Facebook in Thailand’.  		 			A Leeraphong 		,  		 			A Mardjo 		.  	 	 		\textit{Business and Management}  		2013. 1  (4)  p. .  	 	 (Journal of Economics) 
\bibitem[Meng-Hsiang et al. ()]{b42}\label{b42} 	 		‘Understanding online shopping intention: the role of four types of trust and their antecedents’.  		 			H Meng-Hsiang 		,  		 			C Li-Wen 		,  		 			H Cheng-Se 		.  	 	 		\textit{Journal of Internet Research}  		2014. 24  (3)  p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
