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Perceived risk of online shopping behaviour in China

October 2013

Abstract

The fast development of Information and Communication Technology (ICT) has opened many new markets and industries for business. e-Business is one of the most flourishing areas of growth in the twenty-first century. Consumers have followed new fashions and adapted a modern lifestyle. Considering the importance of the Chinese market and the large population of China, this dissertation tries to give an evaluation of what factors could have an influence on the perception of risk in the case of Chinese online shopping. Consumer behaviour has been studied for decades by both academics and industry. There are many successful consumer behaviour models such as the Theory of Research Action (TRA). In the classic models studying perceived risk, the six dimension model is found to be successful for the interpretation of overall perceived risk. These six dimensions are financial, functional, hazard, social, time and psychological (Stone and Cronhaug, 1993). This research combines the social and psychological risks together and incorporates a new factor, privacy risk in order to build a new six-dimension model. For each risk the sum of the product of possibility and importance of the six factors is actually the overall perceived risk. This research employs a typical quantitative method: a survey to study the risk perception of Chinese consumers. Over 500 valid questionnaires were collected. Three linear regression models were built: one overall risk model and two decomposed risk models. In the overall risk model, financial risk, performance risk, physical risk, social and psychological risk and privacy risk were found to be significantly negatively associated with online shopping behaviour, however the time risk was not. In the decomposed risk models, similarly, independent variables were reduced to six factors and it was found that financial risk, social and psychological risk and privacy risk all have a negative impact on shopping frequency; while performance risk, physical risk, social and psychological risk and time risk have a negative impact on shopping expenses. In terms of the four control variables, age, gender, experience and income, significant differences were observed in the measurement of perceived risk. This means different people of different characteristics perceive risks in the own way.

AcknowledgementTable of Contents

11.Introduction

11.1Background of e-Business and online shopping

21.2Research on online shopping

41.3Research aims and objectives

51.4Structure of dissertation

62.Literature Review

62.1Online Shopping Behaviour

92.2Consumer Behaviour Models

112.3Perceived Risk

112.3.1Definition of perceived risk

112.3.2Measurement of perceived risk

132.3.3Dimension of perceived risk

142.4Perceived risk of online shopping

142.4.1Perceived risk in online environment

172.4.2Origin of online perceived risk

182.5Summary

203.Methodology

203.1Research Strategy

203.2Research framework

213.3Research questions

253.4Research method

283.5Data

314.Analysis

314.1Descriptive analysis

364.2Reliability analysis

374.3Validity analysis

394.4Factor analysis

404.5Regression analysis

404.5.1Overall risk model

424.5.2Decomposed risk model

454.6Demographic variables

475.Discussion and conclusion

475.1Review of dissertation

495.2Conclusion and recommendation

515.3Limitations

53Reference

60Appendix

31Table 2 Data description for demographic variables

34Table 3 Online shopping behaviour for all respondents

35Table 4 Descriptive statistics for statement scores

37Table 5 Reliability analysis results

39Table 6 Validity analysis results

39Table 7 Validity test resutls for each facet

40Table 8 Factor analysis results

41Table 9 Regression summary for the overall risk model

42Table 10 Factors for decomposed models

43Table 11 Regression summary for the frequency model

44Table 12 Regression summary for the expense model

45Table 13 ANOVA results for demographic variables

15Figure 1 A model for online perceived risk

21Figure 2 Research framework

32Figure 3 Age and gender for all respondents

33Figure 5 Monthly income for all respondents

34Figure 6 Distributions of online shopping frequency and expenses

1. Introduction

1.1 Background of e-Business and online shopping

The Internet plays a significant role in many industries and is integrated into nearly every aspect of management such as communication by email. Our social life style, communication behaviour and even consumer behaviour have all been changed by the Internet. In fact, e-Business as a new business model has emerged in most industries. This short period in the history of market has seen a total change to our daily life in many different areas. People nowadays may purchase goods much more easily by using online shopping (Bonn et al., 1999). Online shopping could be seen as a new fashion for consumers especially for the younger generation (Thomson and Laing, 2003).A report from Forrester (2011) examined the rapid growth of the Internet retailer in Europe from 2010 to 2011. The size of the online business market had increased by 18 per cent (2009) and 13 per cent (2010). With the improvement of this global online shopping environment, the Chinese online shopping market has also dramatically increased. According to the Bains report (Hoffmann et al., 2012), Chinas e-Business market was 460 billion CNY in 2010 and approaching 1.5 trillion CNY, which has the potential to be the worlds largest e-Business market. It is going to be tripled in three years. The consumer-to-consumer (C2C) and business-to-consumer (B2C) platforms are widely adopted by enterprise in different industries and therefore the number of participants has rapidly increased. The fast growth of C2C and B2C platforms has brought with it a rapid development of two online applications, namely online payment and online banking. The C2C online shopping pattern requires online payment. And the online payment is also increasingly used in B2C online shopping. Xues (2010) report demonstrates the rapid development of online payment and online banking. Netizens utilisation rate of the two applications has reached respectively 22.5 per cent and 24.3 per cent. In particular, with regard to online payment, the number of users, within half a year, reached 23.79 million, with a semi-annual growth rate of 71.7 per cent in China. Not only the rapid development of the Internet but also the development of telecommunication technology, has brought with it a rapid growth of online shoppers. Recently, smart mobile phones have taken up the majority of the cell phone market around the world, and these smart phones usually use the 3G/4G network for accessing the mobile Internet. Overall, the speedy development of the Information and Communication Technology (ICT) has strongly boosted the growth of online shopping users worldwide.

The fast expansion of online market has called the importance of research on online shopping. And because China is second largest economy and the largest emerging market in the world and probably going to be the largest e-Business market (Hoffmann et al., 2012), the research on Chinese online shopping market seems rather necessary.

1.2 Research on online shopping1.2.1 Online shopping experience

Convenience is one of the advantages to attract Internet shoppers (Sin and Tse, 2002). Customers do not need to waste lots of time on searching, comparing, and purchasing (Raijas and Tuunainen, 2001). They do not have to go to a physical store which obviously generates expenses in either traffic or parking. And even for after-sales customer service, if they do not like what they have got, customers can go to their online account, click on refund or return button and print out the labels and send the parcel back. Besides its convenience, online shopping provides abundant information for both merchants and customers. For merchants, they can collect a large amount of consumer shopping data. By the use of appropriate analysing techniques such as data mining, they can get patterns of consumption and then develop a marketing strategy to a specific target, which can be more efficient and effectively implemented. For consumers, handy information makes comparison and decision making easy. They can also share this information with their friends through social networking sites such as Facebook and Twitter. It is a communication channel (Li et al., 1999). The other form of e-Business, represented for example by eBay, can also act as a trading platform between users who want to sell or buy their personal items. eBay only charges the seller for a successful deal made.

Different online shoppers may focus on various aspects of the shopping experience (Li et al., 1999). For example, some online shopping consumers may give more attention to the prices while others may focus on the after-sale services. Successful merchants are those who make their customers feel satisfied and increase the volume of repeat purchases. Therefore, online marketing nowadays attracts increasing attention from practitioners and scholars. Studies of the online consumer experience are increasing.

1.2.2 Online shopping behaviour and perceptionGabriel (2007) and Flick (2009) tried to identify the factors affecting consumers purchasing behaviour or preference. Clearly, a negative experience will reduce trust and a positive experience will positively influence the perception of a vendors trustworthiness. An Oxford Internet Survey carried out by Dutton and Shepherd (2006) shows that the major factor effecting trust in online shopping is previous experience. Previous experience of online shopping determinates the effect of using the Internet to purchase. Similarly, Connolly and Bannister (2007) have provided evidence that experience strongly moderates online consumer perceptions of online vendors competence, security, and integrity. Their findings showed that not only is experience a strong predictor of online consumer trust beliefs, but it is more influential than perceived vendor competence and evidence of vendor security controls (Connolly and Bannister, 2007, p.243). In fact, rapid development of online shopping, at the same time, gives birth to potential risks of online shopping. Risk is frequently encountered in the online shopping environment. Even a small negative Internet transaction experience will consistently affect subsequent beliefs related to that environment.

Online shopping brings disadvantage and sometimes it will become a barrier to the further development of online shopping. Although the description could be as clear and precise as possible, the perception of buyers may still be unclear or inaccurate. Without trying the goods and without previous experience, online products are unlikely to meet the expectation of buyers one hundred per cent of the time. This could be regarded as a kind of perceived risk. Also personal information stored on the website and payment security could be a problem for online business. A report shows that fraudulent card-not-present transactions, where personal data is stolen on the internet, account for about sixty per cent of the losses from credit card fraud (BBC News, 2013a). This brings losses to both merchants and customers.

These two kinds of perceived risk can be called performance risk and privacy risk according to studies of online shopping behaviour (Forsythe and Shi, 2003; Martin and Camarero, 2009). In fact, scholars have found more risks in consumers perception. Cox (1967) finds that perceived risk is associated with financial and social psychological issues. Peter and Tarpey (1975) find time loss or time risk is what consumers are concerned. Stone and Cronhaug (1993) then define a six dimension model to study perceived risk which are financial risk, function risk, physical risk, time risk, social risk, psychological risk. However, most research in the past focuses on general shopping behaviour but does not adapt to the changes in the internet environment. In the fast changing environment, many additional issues such as online privacy have not been well addressed. Studies on Chinese consumers are even fewer. Considering the large population and potential online customers in China, it is especially important to study the Chinese behaviour in online shopping and their perception on risks they may face to. Then it brings the aims and objective of this dissertation.1.3 Research aims and objectives

The emergence of the Internet has brought great changes to our shopping behaviour. It is widely accepted that online shopping is completely different from traditional shopping. This dissertation aims to investigate the online consumer behaviour in China. Many previous studies about the consumers behaviour could be found in the database. However, few if any studies have focussed on one specific factor that affects consumers purchasing decision. The perceived risk has been found to have a great impact on online shopping behaviour in literatures, which will be further discussed in following sections. In addition, studies of online shopping in emerging markets are absent. Therefore, this dissertation will focus on the Chinese online shopping market, which has increased rapidly over recent years (Hoffmann et al., 2012). Details of the research objectives could be described as follows: Understand the factors that influence online shopping behaviour and the perceived risk;

Examine perceived risk more fully and understand its components;

Based on theories, test hypotheses about perceived risk on Chinese online shopping consumers;

Give suggestions to merchants on how to minimise the risks and benefit their business.

The research method is conducted using an online survey to collect data on consumer behaviour and measures of perceived risk and other factors. The sample size of valid return questionnaires is 515. Finally, the findings on how perceived risk influences online behaviour is summarised and conclusions are given.

1.4 Structure of dissertation

This introductory chapter gives a background overview of e-Business and online shopping. It identifies whether perceived risk is a major issue of online shopping. Chapter 2 mainly discusses previous literature and examines both theories and empirical studies regarding online shopping behaviour. It provides a comprehensive review of the factors which may possibly affect online shopping behaviour, paying particular attention to the perceived risk which is the focus of this dissertation. This chapter also introduces some theoretical models and frameworks to study perceived risk. In Chapter 3, the methodology of this research is going is introduced. This includes the research approach, the research framework, hypotheses, data collection methods and an outline of the sample group. The fourth chapter is the main chapter and presents the results from the questionnaire and the results of statistical tests, which are then discussed and interpreted. Finally, Chapter 5 summarises the findings and gives some conclusions on what will affect the perceived risk of online shopping among Chinese consumers and maybe some suggestions for companies of how to minimise the perceived risk in order to benefit their e-Business.

2. Literature Review

The online shopping market continues to undergo rapid development in 2013. A new report shows that there are 242 million web consumers in China and the total number of online transactions has already reached 1.26 trillion CNY in 2012, which is a 66.5% growth since 2011 (CNNIC, 2013). Online retailing accounts for about 6.1% of total retail sales and will continue to shows an uptrend in the future. Reports indicate that average online expenditure in China reached 5,203 CNY (equal to 580 GBP) per year. Online consumers on average purchased 18 times per year in 2012, which is 3.5 times more than that was in 2011. The most popular online goods were apparels where 81.8% of web consumers bought them in last year. Grocery and digital products were the second and third most popular respectively after clothing; 31.6% of consumers reported wishing to purchase grocery products while 29.6% of people bought digital products. As mentioned, the development of ICT and the use of 3G/4G have boosted online shopping. Most recently, 55 million people have shopped online using their mobile phones in China. More than 50% of Internet users will browse online stores when they are free at home or even in the office.

CNNICs report sheds light on the outcome of the quick development of online shopping in China and also demonstrates the statistics of the promising situation of Chinese online consumer market. This section will review some relevant literature about online shopping behaviour especially the perceived risks of online shopping users and outline the theoretical framework of this dissertation.

2.1 Online Shopping BehaviourFjermestad and Romano (2006) analysed fourteen empirical studies and identify the factors that strongly affect customers web purchasing behaviour. They indicate that some important factors such as the external environment, demographics, personal characteristics, the features of e-store, online shopping attitudes and behavioural intention may influence consumers decision on whether they purchase products or services online or not. Netizens who are the internet users, especially the young generation, are much more open to online shopping because they have previous experience of the Internet and purchasing online (Bellman et al. 1999; Bhatnagar et al. 2000). In addition, users who accept the new technologies and who consider the Internet as an effective tool may be more likely to purchase online (Bellman et al. 1999). Using the online brokerage industry as a case study, Chen and Hitt (2002) found a positive relationship between product variety and attrition behaviours. Khalifa and Limayem (2003) also reveal that more convenient and effective online applications, such as transaction systems, could attract more customers to shop online. Perceptions about the Internet and customer service have been considered to be effective predictors of shopping behaviours.

Another strong predictor of online shopping behaviour is the intention. Park and Kim (2003) indicate that factors like information quality, user interface quality and security issues are the most significant to motivate users to purchase online according to a large-scale survey study. Moreover, Hong et al. (2004) investigated the relationship between purchasing behaviour and the information display format. They found that a user-friendly information display and an effective shopping search tool are significant to online shopping. Online shopping sometimes may be hedonic or utilitarian according to Childers et al.s (2002) study. They found that the attitudes of online shopping may be influenced by enjoyment, immersive perception, interactive experience and substitutability.

Moreover, external environmental factor, namely the competitors performance, is a significant predictor of online shopping users behaviour. According to Ramaswami et al.s study (2001), the quality and performance of an offline financial service agent is important to maintain the relationship between online consumers and the financial agent, which in turn could drive the customer to purchase financial produces online.

Limayem et al. (2000) conducted a large-scale survey and concluded that the variables of subjective norms, attitudes and beliefs positively influence the decision to shop online based on the Theory of Planned Behaviour (TPB). Their findings gained support from the later study. Hsu et al. (2006) investigated the online shopping behaviour by employing the Theory of Planned Behaviour (TPB) and the Expectation Disconfirmation Theory (EDT). This study seeks to integrate these two theories and identify the factors influencing online shopping. They find that factors like attitude, subjective norms and perceived behavioural control determinate the acceptance of web purchasing while factors like disconfirmation and satisfaction determine the repeat online purchasing.

Besides the Theory of Planned Behaviour, consumer behaviour is frequently investigated based on the Transaction Cost Economics (TCE) theory, which was initially presented for the organizational studies from an economic perspective. The transaction cost here implies six variables namely product uncertainty, behavioural uncertainty, convenience, economic utility, dependability and asset specificity rather than the real transaction cost in the payment of transaction. Teo et al. (2004) indicated that the transaction cost in both China and U.S is positively related to the willingness of web consuming although slight differences may exist between two countries. They notice that American customers perceive more convenience and economic utility while Chinese consumers find more product and behavioural uncertainty.

Mummalaneni and Meng (2009) focussed on the shopping behaviour of Chinese consumers. They carried out a survey of 237 university students in Beijing and the outcome showed that Internet skills and perceptions challenge young college students and determine their shopping behaviour to some extent. The research findings suggest that the usability of webpages is important to attract more customers especially for the younger generation. Bai et al. (2008) explored Chinese consumer behaviour from a different perspective to that of Mummalaneni and Meng (2009). In their study, two major qualities (functionality and usability) of the shopping website are identified. Functionality implies that a website should include various functions such as transaction, contact and destination that fulfil customers requirements. Usability emphasizes the technological issues of a website including language, friendly layout and graphic, user interface and navigation. This indicates that the quality of a website could directly influence customers satisfaction and ultimately motivate customers to purchase. Nonetheless, some limitations of the case selection and methodology exist in their study and may limit the implementation of it to other online shopping industries.

Zhou et al.s (2007) study provides evidence about the characteristics of a website. This study identifies many factors, by group, which have been previously indicated as significant factors affecting shopping behaviour. For instance, male consumers purchase more and spend more time on online shopping (Alreck and Settle, 2002; Rodgers and Harris, 2003). Bagich and Mahmood (2004), interestingly, claim that rich web users tend to buy more online. The principle and initial reason for online shopping should be its convenience, but nowadays online customers focus more on the recreational element and on competitive lower prices (Donthu and Garcia, 1999). Mauldin and Arunachalam (2002) found that previous perception is an important element in repeat online shopping; also, a comfortable feeling towards the Internet supports web purchasing. Same studies demonstrate that repeat purchasing behaviour may last for a while. People who shop online frequently are more likely to purchase online in the future (Cho, 2004; Yang and Lester, 2004) and previous satisfaction is positively related to the tendency of web purchasing (Pires et al., 2004). Nevertheless, there is still a lack of consensus on several factors namely the age group, education level and Internet usage, and how these affect online shopping behaviour.

Negative factors may also have an impact on both customers and merchants in the process of online shopping. The principle negative factors of shopping online should be the risk of privacy and security. BBC News (2013a) reported instances of customers personal information being divulged and credit card fraud. BBC News (2007) launched a report which indicated that the majority of citizens are worried about those web portals which sell their information (such as home and email address, contact number and so on) to the third party without permission. In fact, this report is covering old ground. Miyazaki and Fernandez (2001) already noticed this as being an issue and found that both fresh and experienced citizens have placed emphasis on the perceived risk to privacy and security with web purchasing. Chang et al. (2005) carried out a comprehensive literature review on the theme of online shopping behaviour. Finally, they identified all possible factors that have an impact on online purchasing and categorized them into several groups: perceived characteristics of the web channel, website and product characteristics and consumer characteristics. All of those factors may effectively influence the customers behaviour through web purchasing.

2.2 Consumer Behaviour Models

The relationship between attitudes, intentions and behaviours has been discussed in depth in many literatures that focus on consumer behaviour, and it is recognised that all three notions interact with each other in relevant studies. There are several well-known theoretical models widely used by researchers. They are the Theory of Research Action (TRA), the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM) and the extend TAM model namely the Unified Theory of Acceptance and Use of Technology (UTAUT).

The aim of the Theory of Reasoned Action (TRA) is to explain volitional behaviours. Its explanatory scope excludes a wide range of behaviours such as those that are spontaneous, impulsive, habitual, the outcome of craving, or simply scripted or mindless (Dillard and Pfau, 2002). Such behaviours are excluded because their performance might not be voluntary or because engaging in the behaviour might not involve a conscious decision on the part of the actor. The TRA model also excludes from its scope those behaviours that may require special skills, unique opportunities or resources, or the cooperation of others to be performed. One may be prevented from performing a behaviour because of a skill deficit, lack of opportunity, or lack of cooperation from others and not because of a voluntary decision not to engage in the behaviour. Later, the TRA model developed into the Theory of Planned Behavior (TPB) by Ajzen (1991).

The Technology Acceptance Model (TAM), as its name implies, explains the customers perspective to the acceptance and usage of a new technology (Davis, 1989). Numerous external factors influence the perceived usefulness and perceived ease of adaptation in a new technological context. This model successfully copes with the problem that beliefs in both TRA and TPB are difficult to appraise, but the value of perceived usefulness and perceived ease could be easily assessable in this model, that stems from the expectancy theory and self-efficacy theory. Later, Venkatesh and Davis (2000) further developed the TAM model to TAM2 by considering the social influences, which included some subjective norms, images, experiences, voluntaries and other set variables. The TAM2 model was developed further, into the TAM3 model, which contains anchor and adjustment variables.

The most comprehensive model that combines the TRA, TPB, TAM, TAM2, the motivational model, the model of personal computer use, the diffusion of innovations theory and the social cognitive theory together has attracted the attention of scholars. Venkatesh et al. (2003) conducted an empirical research based on the UTAUT model and found that this model is much more useful than other individual theories employed separately.

2.3 Perceived Risk

2.3.1 Definition of perceived risk

The concept of perceived risk was introduced by Prof. Raymond Bauer at Harvard University in 1960. He believes that all purchases could bring about an unhappy outcome which are not expected before the purchase. Therefore in his definition, he regards the perceived risk as the uncertainty born with products and services of purchases. After that, many scholars have developed and extended the concept of perceived risk. Cox (1964) emphasises the perceived risk which happens before the actual purchase. It is the perceived losses which the unfavourable uncertainties and bad outcomes bring. Cunningham (1967) basically agrees with Coxs (1964) definition and thinks the key elements of perceived risk are uncertainties which are based on the consumers subjective judgments about what may happen and the possible consequence when the unfavourable uncertainties have happened. Baird and Thomas (1985) later defined perceived risk as the personal evaluation of risks or scenarios about the probability of uncertainty and degree of control. Dowling and Stalin (1994) believe the perceived risk arises during the process of purchases when consumers feel uncertainty and the possibility of bad consequences. Put more simply, from another point of view, Mitchell (1999) regards the perceived risk as the subjectively expected losses. This means consumers have sensed a kind of probable or possible loss which they cannot manage and control. In the subsequent empirical studies, many papers follow the definition of Cox (1964) and Cunningham (1967).

2.3.2 Measurement of perceived risk

Many scholars have proposed various methods for the measurement of perceived risk. The two dimensional model from Cunningham (1967) is one of the earliest models. The two dimensions are uncertainty and danger and so the perceived risk is the product of them: perceived risk = uncertainty of losses severity of consequences. A ten point rating scale is used to describe the degree of uncertainty and danger. Since the introduction of this model, it has become a classic but academics have wide discussions on whether the relationship of uncertainty and danger is addition or multiplication. Sjoberg (1980) criticises that the multiplication comes from economic theories but it may be correct in consumer behaviour. Stone and Grounhang (1993) argue it from a practical way that in the process of purchase decision making, consumers would not actually use numerical measurement to calculate the perceived risk.

Bettman (1973) distinguishes between two kinds of perceived risk inherent risk and handled risk. Inherent risk is the latent risk a product class holds for a consumer, the innate degree of conflict the product class arouses in the consumer. Handled risk is the amount of conflict a product class engenders when the buyer chooses a brand from that product class in his usual buying situation. Thus handled risk includes the effects of information and risk reduction processes as they have acted on inherent risk. But Cunningham's (1967) measure only deals with inherent risk. So he uses an additive model rather than productive model to measure perceived risk. Even the coefficients and R-square value are very close, the addictive model suits better.

Peter and Ryan (1976) measures both possibility and importance of losses and link them to brand preference. where is the perceived risk for brand ; is the possibility of losses when purchasing brand in facet ;the importance of losses when purchasing brand in facet ; is the facets of risks. This model has been tested in some studies and its reliability and validity are verified (Mitchell, 1999).

Dowing and Stalin (1994) split the overall perceived risk (OPR) into product class risk (PCR) and product specific risk (SR) and their model resolves as . Based on that, they proposed an important concept acceptable risk (AR). Then during purchase, when the product specific risk is larger than his acceptable risk, the consumer will keep searching for other information or products until the perceived risk is under the limit of acceptable risk. During this process, the perceived risk is dynamically changing.

Academia has not yet reached agreement on which measurement is the most effective one. From applications, the two dimensional model has been used widely and accepted by many scholars but it is still not clear whether the relationship should be addition (+) or multiplication ().

2.3.3 Dimension of perceived risk

Dimensions of perceived risk are usually regarded as facets or aspects in some literature. They include what kinds of risks are inherent in perceived risks or simply the types of risks. What perceived risk consists of is a major task of studies of perceived risk research. In the early days, Cox (1967) and Cunningham (1967) started the exploration in the late 1960s. Following that, the studies became deeper and wider and expanded from just conventional shopping, to telephone shopping, mail shopping and online shopping, with the addition of many new dimensions. They are all found to contribute to perceived risk in purchases. Some examples include Cox (1967) findings that perceived risk is associated with financial and social psychological issues. Cunningham (1967) finds that perceived risk originates from the gap between what consumers actually get and what they expect. These gaps are losses from time, health, finance, social consequence and functions of products.

Then in 1968, Woodside clarified the issue reporting that perceived risk can be divided into three dimensions: economic risk, functional risk and social risk. Roselius (1971) argues that when consumers are making decisions they hesitate because they are considering their potential losses. There are four types of losses in purchases. Time loss: if consumers are not satisfied with the products, they have to spend time on choosing another one, repairing or refunding it. Hazard loss: certain products may be harmful for consumers safety, such as food and toys. Money loss: the money spent on resolving problems or buying a new satisfactory product. Ego loss: when consumers have got a faulty product, they will feel embarrassed because of the failure in purchase. The time loss is also mentioned in the research of Peter and Tarpey (1975). It shall include the losses of time, effort and energy contributed to the purchase.

However, Bettman (1973) proposed a different way of risk classification. He believes risks come from two sources: one induced by the type of products and the other brought by the brand. Here is a simple example. Consumers may feel risks in choosing laptops. They are worried about whether the laptop will work reliably all the time as it carries large data. But some consumers may feel more comfortable when they like a brand such as Apple because the service and quality are assured. The former type of risk is inherent risk but the latter one is handled risk. In this case, the inherent risk is higher than the handled risks but in cases where consumers have no information at all, these two risks are equal.

In 1993, Stone and Cronhaug proved that six types of risks existed in purchases and built a framework of the six dimension model. These six dimensions are financial, functional, hazard, social, time and psychological. In their study, they also found these six components do not act independently but the former five have an impact on psychological risk and then finally affect the overall perceived risk. Therefore until now, this six dimension framework has been applied in many consumer behaviour studies. And this is the model which has been chosen as the framework for this research.

2.4 Perceived risk of online shopping

2.4.1 Perceived risk in online environment

The online environment is very important to online customers. The security problems, the lack of trust in Internet retailers, and the lack of Internet knowledge may form the use of Internet shopping. Jones and Vijayasarathy (2001) suggest that individuals have unfavourable perceptions of Internet shopping security as they are wary of giving credit card details over the Internet and Aldridge and Rowley (1998) argues that businesses should provide alternative arrangements. For example, consumers should be able to make arrangements to pay by phone, fax, or post and they should apply encryption for their credit card numbers and should use electronic cash by withdrawing digital money from an Internet bank and storing it on the hard disk. Aiming to synthesise the relevant literature, Forsythe and Shi (2003) developed a conceptual model for the types of perceived risk and demographics on online shopping behaviour, which contains six types of perceived risk (see bellowed Figure 1).

Figure 1 A model for online perceived risk

Source: Forsythe and Shi (2003)

These are: the financial risk, the product performance risk, the social risk, the psychological risk, the physical risk and time/convenience risk (ibid).

As one of the crucial barriers that confront e-commerce behaviour, perceived risk has a significant influence on consumer decision-making. Andrew and Boyle (2008) asserted that as a result of perceived risk, consumers in many cases, would be reluctant to make a purchase to maximise utility. That means even consumers who believe that shopping online is cost-effective, may still refuse to use it, if they are affected by such a stereotype that Internet is an unsafe medium for shopping purposes.

Inevitably, online purchasing can be viewed as risky in the uncertain context of online retailers. The complexity of the network environment and the uncertainty about Internet transactions run on open platforms has increased the risk of e-business (Pavlou, 2003). Online transactions, compared to online shopping, are much more impersonal and anonymous, therefore building trust as much as possible, to reduce risk, plays a crucial role in online vendor business performance and customer retention. When consumers are making purchase decisions they have to rely on what they see based on the information they get on the website before confirming purchase. Thus, building higher trust with costumers will lead to quicker and easier purchase decision making by consumers (Chen and Barnes, 2007).

Privacy in online service, as mentioned above, is an issue of considerable concern because spilling out personal information and losing control of their own data is the last thing consumers want to see. Operated under safety agreement and security policy, Internet transactions may lead to the problem of losing control. Moreover, privacy policies vary from one online retailer to another. Some will apply to collect consumer date for internal use only, while others may operate a scheme to sell customer information for profit (Grewal et al., 2004). Consumers demand to know clearly for what purpose user data is collected, how long it will be kept and if there are any consequences (Pikkarainen et al., 2004). Unfortunately, it is extremely difficult to obtain this reassurance due to the non-transparent website policies that are widely adopted.

Although perceived risk exists in both online and traditional shopping behaviours, the details are quite different. When shopping online, customers are usually unable to properly assess the true value of products by touching or feeling the quality, the materials and other relevant elements. Moreover, the absence of social contact and interaction with other people may lead to feelings of discomfort. Perceived risks in the online context could easily arise from security issues of Internet using, online transaction and privacy information (Martin and Camarero, 2009).

Nevertheless, different customers have different perceptions of risk. When academics discuss the risks of online shopping, actually it is not the objective risk but the perceived risk which is discussed. Rather than reasoning, customers perceive whether something is risky by their intuition. Previous experience influences peoples judgment; therefore, the judgment varies because of the different opinions about the price of products, the level of involvement and so on. Martin and Camarero (2009) indicate that some customers may notice the potential risks of web purchasing but they also appreciate the advantages of buying online because online stores provide a convenient way for comparing prices and the quality of products and obtaining sufficient information to make the most reasonable purchasing decision.

Either traditional storeowners or online retailers should notice the importance of gaining satisfaction and trust in the business context. Online retailers should improve their commerce and the loyalty of consumers by creating trust. However, the construction of trust in an online context is much more difficult than in an offline context. Firstly, it is a fact that many online shores are offering the same products or services; customers can easily switch from one online store to another, based on lower prices or other factors. Therefore, severe competition should be worth more attention. Furthermore, customers nowadays can purchase worldwide. Hence, the online retailers face not only domestic competition but also global.

Many factors such as the quality of a website, customer service and previous experiences of online purchasing contribute to reduce the perceived risks of online shopping. This dissertation will find out the approaches that can be used to ease the perceived risk based on empirical evidences.

2.4.2 Origin of online perceived risk

Online shopping takes advantage of the openness of the internet and makes online transaction cheap and information easily available. The internet gives equal opportunities to all participants including sellers and buyers. Consumers can use search-engines to gather valuable information, at a low cost to themselves but with high efficiency. Therefore the asymmetry of information between consumers and merchants may be reduced. However it does require the online shopper to have good searching techniques and it takes a considerable time to develop this skill. The rational and optimal choice therefore depends on the ability and skill of consumers. Furthermore, there is some other asymmetric information born in online shopping. First, the identities of parties are not clear. In conventional shopping, normally the two parties involved, the sellers and buyers, make a deal face to face. But when online, the two parties can only rely on the provided information and they are actually just two nodes on the internet. Online fraud is easy because consumers do not know who the seller is, what characteristics the seller has or how long the site will stay there. Secondly, the authenticity of information may be a problem. In conventional shopping, consumers can see, touch, hear, smell and sense the quality of products. However online products only exist as digital codes before they are received, and a 2-dimensional picture display can induce visual bias compared to a real display in space. Third, online privacy is a critical issue. In conventional shopping, money and products are the only things to be exchanged. It is not necessary for consumers to provide other personal information. But online shopping usually requires at least payment information and delivery information which is sensitive and can be used by criminals.

According to the source of risk, Cases (2002) summarises perceived risk of online shopping as being product related, website related, internet related and remote transaction related. Lim (2003) divides perceived risk in four categories: technology, product, seller and buyer. In Einwillers study (2003), there are only three sources: from electronic transaction systems, from online merchants, and from buyers themselves. But Liang and Huang (1998) argue that the uncertainty actually comes from two sides. One is the product when it does not match the expectation of consumers. The other is the transaction process where consumers have no control or no faith in it. Obviously, these two sides will increase the risks of online shopping.

2.5 Summary

This chapter has included an extensive review of existing literature about online shopping and consumer behaviour, paying particular attention to perceived risk. With regards to online shopping, the external environment, demographics, personal characteristics and features of an online store will all affect a consumers attitude and behavioural intention and so have a final impact on consumer behaviour on online shopping. Researchers started to investigate perceived risk of purchases in the 1960s and since then some classical models (such as the two dimensional model) have been proposed to explain consumer perceived risk when making purchases. But these studies have been for general purchases. In the environment of online shopping, some factors are not valid and some factors, such as payment issues, are added. Finally the conceptual model from Forsythe and Shi (2003) which covers product performance risk, financial risk, physical risk, social risk, psychological risk and time risk is suitable to study the perceived risk of online shopping. Online shopping in China started later than in Western countries but it has been growing very quickly in the recent decade. Academics so far have not drawn enough attention to this change. Some papers only focus on general online shopping behaviour. This dissertation will investigate the perceived risk of online shopping from these those six dimensions and address some of the problems facing Chinese people and their online shopping behaviour.

3. Methodology3.1 Research Strategy

Basically there are two widely used approaches in research: qualitative and quantitative analysis. They each have their own advantages and disadvantages. Qualitative methods are more suitable for examining possible relationships, causes, effects and dynamic processes (Hughes, 2012). Interviews are frequently used in qualitative research, and by using in-depth questions and answers from interviewees, researchers can investigate the how and particularly they why of decision making. In understanding human behaviour and psychology, it is very useful. However the data it uses is difficult to collect and time consuming and case specific sometimes. So the validity and reliability is hard to control (Hughes, 2012). Quantitative methods, in contrast, usually employ statistical, mathematical and computational tools on a large scale of data. It can be used to obverse the general trends and allow repeated experiments. Hypothesis testing is frequently used. There has been a lot of time spent and a wide debate conducted on these two methods, but a suggestion from Bryman (1988)that combining the two is preferred to eliminate their disadvantages. In this research aims to investigate the relationship between perceived risks and online behaviour in a general sense, and for this subject there are already some hypotheses, therefore it is believed that a quantitative approach is better for this research.

3.2 Research frameworkThis dissertation will investigate the relationship between consumers online shopping behaviour and perceived risk. Jacoby and Kaplan (1972) define perceived risk from five aspects: financial, performance, physical, psychological, and social risks. Peter and Tarpey (1975) then added a new dimension time risk. It is believed that these six dimensions can cover all factors in perceived risk. Stone and Gronhaug (1993) define perceived risk by the subjective expectation of losses. The more expected losses there are, the larger risk one perceives. From their empirical results, they conclude that the six dimensions financial, performance, physical, psychological, social and time risk can explain 88.8 per cent of total perceived risks. To date most of the research has applied this six dimension structure in studying perceived risk, and therefore this research follows the same six dimension structure.

Besides, there are demographic factors which may have an impact on the measurement of perceived risks, such as gender, age, experience and income. It is understandable that men and young people are more likely to take risks and when they get more experience in life, they become risk averse. Then these four factors are also added to the model. Finally, the framework of this research could be illustrated in the figure below. Details of hypotheses are given in next section. Figure 2 Research framework

3.3 Research questionsTo study consumer behaviour, this study uses a direct measurement, recording the average time spent online shopping in a month which could adequately describe the frequency of online shopping. For the four control variables, the basic information of consumers is collected. The five external factors and their sub factors, which are the dimensions of perceived risk, are examined through several questions in the questionnaire. Then the scores are calculated and the internet use for shopping and associated behaviour can be estimated.

Finally, in line with previous literature, several hypotheses are formed and tested in the analysis.

Financial risk is the perception that a certain amount of money may be lost or economic losses caused by some reasons (Garner, 1986). In the online environment, unlike conventional shopping where transactions are made face to face and hand by hand, it normally requires payment in advance and delivery after, which causes some risk of default and intentional fraud due to sellers lack of credit. The website of e-stores may not be secure which may cause losses to buyers and sellers alike. Financial risk may be the biggest concern of online shopping. Therefore we have Hypothesis 1. Hypothesis 1: Financial risk is negatively associated with online shopping behaviour.

Performance risk is the risk that a product purchased may fail to function as originally expected (Kim and Lennon, 2000). Customers expectation of a product is unlikely to be the same as what they actually get one hundred per cent of the time. In traditional shopping, functionality could be tested by full examination, however in the long term no one can guarantee that it will last in future. For online shopping, and especially auction style C2C e-Business, there are normally gaps between what you actually get and what you expected and between what you get and how it was originally described. In the new type of e-business, known as group buying, represented by Groupon, volumes of transactions sometimes increase beyond the capability of merchants so there are some complaints about the quality of their services for customers is reduced (Beijing News, 2010). Hypothesis 2: Performance risk is negatively associated with online shopping behaviour.

Physical risk refers to the danger to health or safety associated with the product (Roselius, 1971). But in this research, it is believed that health and safety issues are also associated with the shopping activities. Nowadays, many office men and women have health problems because they spend so much time sitting and facing a computer. Obesity and eye sight problems are common diseases. In contrast with traditional shopping which involves walking and travelling which could be regarded as excise, online shopping sometimes involves a lot of time spent searching and communicating online with suppliers. Though ergonomic equipment could reduce the risk, it is still a concern. Hypothesis 3: Physical risk is negatively associated with online shopping behaviour.

Social risk is the perception that a product purchased may result in disapproval by family or friends (Dowling and Staelin, 1994). And psychological risk refers to a negative effect on a consumer's peace of mind which may be caused by a defective product (Jacoby and Kaplan, 1972). Previous theories treat these two separately. Social risk comes from people nearby but results in mental stress for consumers. It could be seen as a negative effect from others. Therefore in this research the social risk and psychological risk are combined. Furthermore, stress and worries may not only be focussed on the purchased products but also on the behaviour of online shoppers themselves, because in the eyes of conventional people, online shopping is disliked. Their negative opinions towards online shopping could result in stress for online shoppers. Hypothesis 4: Psychological and social risk is negatively associated with online shopping behaviour.

Time risk is not only about time, but also convenience and the effort made in purchases. But they could all result in time wasting in the end (Bauer, 1967). Information is one of the biggest advantages of online shopping, which provides customers with many options to choose and compare with. However, one may find there is too much information sometimes, particularly in searching for a good retailer using a search engine. Therefore, there is some time risk that is associated with information searching, payment clearing, delivery, product exchanging and refunds. Every point in the process of online shopping could consume much time which means there is a great potential of risk associated with it. Hypothesis 5: Time risk is negatively associated with online shopping behaviour.

Besides the above six dimensions of perceived risk, in online shopping, there are some new types of risk attached. Privacy risk is one of important additional concerns. Traditional shopping takes little personal information to complete a transaction. However online shopping requires at least a name, address, online account and payment method in online shopping. If this information is leaked it may cause irretrievable losses to both buyers and the service provider. Online privacy is a large topic for discussion. But clearly the growth in online retail business will shrink or even stop if people stop trusting the websites for shopping or banking (BBC News, 2004). Therefore this factor is added to the hypothesis that privacy risk in the online environment as a kind of perceived risk is negatively associated with online shopping behaviour. Hypothesis 6Privacy risk is negatively associated with online shopping behaviour.

Consumers are one of the major parties involved in online shopping. Their demographics and history of using the internet, experience of online shopping, personal knowledge may all have an impact on the perceived risk. Different consumers perceive risks differently although the same things may happen. Therefore, in online shopping, consumers use their knowledge to process all the collected information and make a judgement on the risk. Characteristics and personalities will definitely affect perception of risks. In studies of consumer behaviour, gender is always a factor that affects the decision making of consumers. For example, Meyers-Levy (1988) comment that gender has a significant impact on purchase decision. Forsythe and Shi (2003) find that males perceive more time risk while females perceive more economic, performance and psychological risk, and as people age, individual perception and judgement change too. In the research of Philips and Sternthal (1977), age is seen as an important factor in information processing. Li et al (1999) argue that young people have more time and better techniques for using computers and the internet so they like online shopping. For people who have some online shopping experience, they may be familiar with its procedures and operations. And if they have had a bad experience in the past, they may have learnt lessons from them and be more skilled to prevent potential losses. Sometimes, online shopping experience can be correlated to history of using the internet. Lohse et al (2000) found that the longer people spend on the internet, the more likely they are to purchase something. The more they rely on the internet, the less risks they perceive. Online shoppers tend to be well educated and have high levels of income (Donthu and Garcia, 1999). Richer people have more funds and are therefore better able to recover from losses so it is not that sensitive to take risks in normal shopping. It is still worth investigating whether gender, age, experience and income will significantly affect the perceived risk.

3.4 Research method

Surveys are one of the most frequently used research methods in business research (Bryman and Bell, 2011). However, Bryman and Bell (2011) also outlined some problems with using social surveys to investigate behaviour. There might be problems of misunderstanding the meaning of questions, omissions, mistakes of memory and the respondent may also have concerns about social desirability and possible threat. And the survey may be affected by the interviewers characteristics. However, for the purposes of this research, it has been decided that a survey is the main method to collect data. There are many advantages such as surveys are easier to conduct, and efficient for the collection of a large sample. A carefully designed survey, with a well-structured questionnaire, will support this research. Besides, in the study of internet related topics, an online survey becomes a particularly powerful tool, as there are fewer limitations which may be associated with online surveys. For other businesses the sample is arguably biased because the respondents have to be internet users first in order to respond. However, for investigating online shopping behaviour, the use of an online survey is very appropriate. Besides, as compared with an email survey, a web survey outperforms because it can reach a wider variety of people in terms of its appearance. Additionally, a professional survey website can provide a functional service to create a multi-layered questionnaire and the results are automatically coded and stored, ready for analysis.

The questionnaire contains two parts. In the first part, there is only basic information such as the respondents age, gender, income and experience, and also an indication of their online shopping behaviour, the average times of online shopping in a month and the average spend in a single purchase. These were both measured by numerical true figures, which were then used to calculate the average expenses on online shopping in a month. Respondents were asked in two questions:

How many times did you shopping online in the last month?

How much did you spend in last purchase online?Actually the first question measures the frequency of online shopping and the second measures the cost of it.

The second section contains a group of questions to describe the factors listed in the research framework. There are three types of scale settings for the answers to the questions: Thurston Scale, Likert Scale and Guttman Scale. The Likert scale is based on the statement. Normally the respondents will express their agreement or disagreement by the degree measured as strongly disagree, disagree, neutral, agree and strongly agree, with marks as 1, 2, 3, 4 and 5 respectively. The Likert scale is good at maintaining the reliability of answers to questions and the ease to measure the perceived risk, and for this reason the Likert scale was applied for the questionnaire.

However the measurement is slightly different from common Likert scale because of the dependent variable, shopping behaviour has two dimensions: frequency and cost, and based on the measurement of perceived risk in the study of Peter and Tarpey (1975), the overall perceived risk is:

where is the overall perceived risk;

is the possibility of losses in facet ;

the importance of losses in facet ;

is the facets of risks.

Here we call the possibility of losses uncertainty and the importance of losses severity. It is better to examine the perceived risk from both ways at the same time. Then there are two sets of Likert scales for one statement, one to measure the uncertainty and one to measure the severity. The lower score indicates a lower level of response and a higher score indicates a higher level of response. In fact, in this way, both independent and dependent variables are decomposed. The uncertainty of perceived risks is reflected by the frequency of the purchase behaviour and by the severity of any perceived risks and these are then reflected by the cost of a single purchase. The measurement of each statement was interpreted as follows (Table 1):

12345

Statement 1UncertaintyLeastMost

SeverityLeastMost

As introduced previously, there are six factors in the framework of perceived risk: financial risk, performance risk, physical risk, psychological and social risk, time risk and privacy risk. For each of the risks the questionnaires had three statements to describe them and the statements were designed to best gather information of these aspects. Details of them are as follows:Financial risk:

Statement 1: payment in advance may incur losses due to many reasons such as loss of parcel or no delivery.

Statement 2: information of online payment methods are stolen by someone may incur losses.

Statement 3: if return postages is not refundable or prepaid, it incurs extra costs in return and exchange. Performance risk:Statement 4: There are fake products and counterfeit online. Statement 5: There are gaps between the real situation and the expectation. Statement 6: There are differences between the real situation and what it is stated on the website. Physical risk

Statement 7: Online shopping involves long time searching for information through computers and mobile devices which causes eye sight problems, back sore and other physical discomfort.

Statement 8: Without pre examination, touch or other senses, the products could turn out to be sharp, smelly and other situations that cause discomfort.

Statement 9: Products from unreliable sources do not pass relevant tests may cause health issues.

Psychological and social risk

Statement 10: there are anxieties in the waiting for delivery.Statement 11: if losses happens, there is mental stress from yourself and others.Statement 12: Traditional people may have negative opinions on online shopping.Time risk

Statement 13: it takes too long to search for products and sellers.Statement 14: it takes too long to receive the order.

Statement 15: it takes too long to return the order. Privacy risk

Statement 13: Personal information may be leaked to the 3rd party.

Statement 14: Purchasing history and interest may be tracked and studied.

Statement 15: After registration, you may be contacted though emails, calls and messages without permission.

The full questionnaire detail is attached in the appendix.

3.5 Data3.5.1 Data collectionIn any business research or statistical analysis, sampling is a key part to ensure the quality of research. As the research objective is the Chinese online shoppers and their behaviour, all internet users who have online shopping experience are the population of this research. The ideal survey would mean we can have the data of the whole population, however that is clearly impossible, and so sampling is required. Sampling is the procedure which draws on a portion of a population and the sample is simply a subset of the population (Zikmund, et al, 2012). There are several sampling methods such as random sampling, systematic sampling, cluster sampling and accidental sampling. When the questionnaire is well structured and designed, the link to it is distributed online through social network sites, forums and communities, email and instant messaging to internet users and my friends and their relations and contacts. So the sample for this study is representative of the Chinese population of online shoppers, through the use of the quota method. However the results may have some bias as it is impossible to distribute the survey link everywhere on the internet. The links were generated from my social networks, and concentrated largely on young people. This could be a limitation with data collection but in order to improve the result would cost more and take longer.

As the following analysis involves statistical testing, in order to ensure the results are reliable, a large sample size is preferable. But as time and cost were both restricted, the target number of valid questionnaires returned was 500 copies.

3.5.2 Data analysis

When the target number of questionnaires were collected, answers to questions were coded and processed in Excel. Firstly the distribution of results was observed. Then the results were tested using a series of analysis including validity analysis, reliability analysis, factor analysis in SPSS. Then there three models were used to test the hypotheses. The integrated model was the main model used. It aims to build the relationship between perceived risk and shopping behaviour. Shopping behaviour measured by the number of times of online shopping in a month and the average expenses in a single purchase is the dependent variable. The measurement of perceived risk follows the definition in the study of Peter and Tarpey (1975), but without brand specification. And for this study the two measures were called uncertainty and severity rather than probability and danger as in the original study. They are the independent variables. Then the model is:

Where ,are the number of times of online shopping in a month and the average expenses in a single purchase;

are the uncertainty of losses in facet ;

are the severity of losses in facet ;

are coefficients;

is the constant, is the error term.

More simply, if replacing and by y and x, the equation becomes:

MACROBUTTON MTPlaceRef \* MERGEFORMAT (1)

The decomposed models investigated one dimension at a time, i.e. the frequency in shopping behaviour is affected by the uncertainty, and the expenses in shopping behaviour is affected by the severity. Then the other two models are:

MACROBUTTON MTPlaceRef \* MERGEFORMAT (2)

MACROBUTTON MTPlaceRef \* MERGEFORMAT (3)

By these three linear regressions, the significance and coefficients of facets were compared and analysed.

After that, the four control variables, gender, age, experience and income were examined and the differences in the mean of measures of perceived risks caused by these four variables were tested in ANOVA analysis. 4. Analysis

The original questionnaire was designed in English and was translated into Chinese because this study focuses on Chinese consumers. The questionnaire was published on sojump.com which is one of the leading online survey service providers in China. The link to the questionnaire was distributed and had been open for responses for one week. Finally there were 515 returned questionnaires which are valid for analysis. The results are presented and analysed and the findings and conclusions are given in the end of this dissertation.

4.1 Descriptive analysis

First of all, it is necessary to have an overview on the general description of the data. The questionnaire was divided into two parts: personal information and risk perception. In the personal information section, to keep it simple, only six questions were asked. The first four questions are actually control variables and Q5 and Q6 are online shopping behaviour measures. Some basic statistics are presented in Table 2 as follows.Table 2 Data description for demographic variablesVariableValueFrequencyPercentageMode

ageunder 187614.8%18-25

18-2523946.4%

26-3515530.1%

above 35458.7%

gendermale28054.4%male

female23545.6%

experienceless than 1 year9718.8%2-3 years

2-3 years24647.8%

4-6 years14728.5%

More than 7254.9%

incomeLess than 20019237.3%Less than 200

200-49917133.2%

500-99911923.1%

1000-2000285.4%

More than 200051.0%

Figure 3 Age and gender for all respondents

Looking at the age groups, the largest proportions were aged 18-25 and aged 26-35, which together accounted for over three quarters of the whole sample. It is true that young people like online shopping. They are more open to new technologies. When smart phones become popular in China, they adapted to the new lifestyles easily in line with the current trends happening in the world. Though the survey did not ask questions about their education, it is nevertheless believed that the group of 18-25 year olds consists mainly of university students and the group of 26-35 is mainly young people with some income. The third group, less than 18 years of age, only makes up 15 per cent of the sample, because they are too young to have many funds for shopping. The group of people above 35 was the smallest group, making up less than 10 per cent. They may be conventional people who do not like online shopping. The results for male and female are expected to be equal but in fact they were not in the sample. There were 8 per cent more males than females in the sample. However, this is not seen as a problem because the sex of the Chinese population is imbalanced due to birth control, and Chinese people prefer boys rather than girls. Currently there are 118 boys for every 100 girls (BBC News, 2013b). So the sample result actually reflects the fact.

Figure 4 Online shopping experience for all respondents

Figure 5 Monthly income for all respondents

Looking at the online shopping experience (Figure 4), the largest group comes from people of two or three years online shopping experience, and this makes up nearly half of the total respondents (47.6 per cent). Combined with the new online shoppers (started shopping within one year), two thirds of respondents are relatively new to online shopping. Only one third have four years or more experience and only five per cent of them have been shopping online for over seven years. Considering that the trends of online shopping and online stores happened much later than in the US and UK, this is a reasonable finding. But this market is fast growing. Generally, the income of Chinese people cannot be compared with the British people because the minimum salary standard in China is only like one tenth that of the UK. Chinese people earn much less (Figure 5). Besides, most respondents are young people in the sample, so there is no wonder that the largest group of income is less than 200.

Table 3 Online shopping behaviour for all respondentsNMinimumMaximumMeanStd. DSkewnessKurtosis

Online shopping frequency5150204.593.561.422.18

Online shopping expenses515352000313.91266.212.067.41

Figure 6 Distributions of online shopping frequency and expenses

Questions 5 and 6 ask for the frequency and expenses of online shopping. In Table 3, it is reported that the respondents did 4.59 times of online shopping and averagely spend 313.9 CNY (about 30 GBP) online. The range of shopping frequency spreads from 0 to 20, which means someone did no online transaction at all in the last month. The largest expense online is 2000 CNY. Compared to its average 313.9 CNY, we can see its distribution is positively skewed (2.06) and most expenses are concentrated around the average (7.41). Compared to expenses, the distribution of frequency is less positively skewed and less peaked but still it cannot be regarded as normal distribution. The standard deviations for both of them are within the range of one mean.

Table 4 Descriptive statistics for statement scores

StatementMeanStd. DevStatementMeanStd. Dev

S1a2.931.107S1b3.541.143

S2a3.311.195S2b3.831.135

S3a3.501.101S3b3.481.113

S4a3.701.169S4b3.671.159

S5a3.511.085S5b3.701.067

S6a3.541.093S6b3.531.084

S7a3.301.185S7b3.261.234

S8a3.421.127S8b3.551.107

S9a3.531.091S9b3.701.102

S10a3.031.250S10b2.931.187

S11a2.991.156S11b3.051.228

S12a2.951.220S12b2.961.239

S13a3.301.165S13b3.201.098

S14a3.161.099S14b3.201.122

S15a3.271.159S15b3.401.113

S16a3.461.168S16b3.781.154

S17a3.381.124S17b3.511.146

S18a3.531.164S18b3.651.199

In the section of the questionnaire looking at risk perception, six facets of perceived risk were described in 18 statements, 3 for each facet. For each statement, answers in A (Table 4) describe the degree of uncertainty and answers in B describe the degree of severity. As all these statements are about risks which are supposed to be disliked, the average scores are expected to be positive (above 3) where a score of 3 is regarded as neutral. However, we can still observe some statements get average scores slightly below 3, which are S1a (2.93), S11a (2.99), S12a (2.95) and S12b (2.96). When going back to the statement, S1 says payment in advance may incur losses due to many reasons such as loss of parcel or no delivery. The reason behind the score may indicate that at this time, most merchants or intermediate platforms such as eBay and Taobao in China have provided full protection on their purchases. In cases of loss, they are covered by services so consumers are no longer worried about this kind of risk. Only in private transactions (such as on Gumtree in the UK), this could happen but still it is rare. S11 says if losses happen there is mental stress from the consumer and others. The score shows this to be neutral (2.99). As online purchases become common, they are no longer seen to be important decisions which bear too much pressure. Similarly, with questions S11 and S12, traditional people are not likely to have negative opinions on online shopping. People have accepted this change in their lives (2.95). Even where negative opinions exit, people tend to be relaxed and solve the problem (2.96).

4.2 Reliability analysis

Reliability refers to the level of consistency of the results and the stability of the results as measured. The behaviour of the respondents cannot be represented accurately if there is an unreliable measurement, and the result of the analysis will also be affected. As a result, it is necessary to conduct a reliability test before the analysis. In this paper, the test will focus on the collected data from questionnaires, to see if the data is reliable. In general, there are mainly three ways to test for reliability: theinter-rater reliability, the test-retest reliability and internal consistency reliability. The first two methods will test the consistency of stability and equivalence across different structures. The third method focuses on the relationships between different content in the subject, and the test will check if they have the same content or quality in the subject. Usually the Split-half reliability, Kude Richardson reliability and the Cronbach's Alpha reliability are used to measure the internal consistency reliability. But the Cronbach's Alpha (Cronbach, 1951) is more commonly applied because this method gives the consistency of the scale for each question score in the questionnaire data. And the reliability of the Likert scale can also be measured by this method. So, in this study, Cronbach's Alpha will be applied. To be specific, if the Cronbach's Alpha is higher than 0.7, the data will be reliable for analysis. When it is larger than 0.9, the result consistency is excellent and when it is below 0.5, it is poor and unacceptable.

Based on the theories and measurement to perceived risk, we do not consider uncertainty and severity separately any more but compute the product of them to interpret the perceived risk of the facet. So from this section, the analysis was carried out based on a new result dataset, which directly measurd the perceived risk. For example, in the new dataset, S1 = S1a S1b, S2 = S2a S2b and so on. The results of the reliability analysis are given in Table 5 below. Table 5 Reliability analysis resultsCronbach's AlphaN of Items

.94518

Corrected Item-Total CorrelationCronbach's Alpha if Item DeletedCronbach's AlphaN of Items

S1.666.942.8323

S2.708.941

S3.736.941

S4.691.942.8593

S5.726.941

S6.729.941

S7.595.944.8213

S8.770.940

S9.717.941

S10.622.943.8693

S11.628.943

S12.552.944

S13.656.942.8083

S14.714.941

S15.693.942

S16.701.941.8193

S17.663.942

S18.657.942

The two left-hand columns show the reliability across the 18 statements. The overall Cronbachs alpha is 0.945, which indicates the questionnaire is well designed and the answers are highly reliable. The correlated items to the total correlation are all larger than 0.55 which means all statements are useful. The two right-hand columns consider each facet of perceived risk separately. All aspects show good reliability for the three statements in each facet (all above 0.8) with very small differences in levels of reliability.

4.3 Validity analysis

Validity usually refers to the accuracy of the measured results and the proximity of the measured results to the target concepts. In a survey, the validity of the questionnaire refers to the extent to which it reflects the theoretical concepts of measurement. The higher the validity of scales, the better the measured results reflect the true characteristics. It is necessary to test whether the classification of statements is valid and whether the influence of scales on consumer evaluation is valid.

There are several different kinds of validity such as construct validity, content validity, convergent validity, representation validity, face validity, criterion validity, concurrent validity and predictive validity. Among them, content validity and construct validity are the most important ones. Content validity is also known as logical validity. It refers the systematic examination of the test content to determine whether it covers a representative sample of the behaviour domain to be measured (Anastasi and Urbina, 1997). When the tested contents of the questionnaire do not cover the full contents of the study, the questionnaire does not have good content validity. However when the items measured in the questionnaire do covers all the research content, then it has good content validity. Generally speaking this will depend on expert judgment, which is to say that the professionals and experts have experience and can assess the accuracy of the questions. Construct validity is also known as structure validity. It refers to the extent to which operationalization of a construct (i.e., practical tests developed from a theory) do actually measure what the theory says they do. Construct validity includes discriminant validity and convergent validity. Between them, discriminant validity is used to measure the distinction between the various dimensions of the adequacy of validity, usually by judging if the corresponding correlation coefficients of each indicator and a factor if higher than that between other factors. At the same time, it determines the indicators within the same dimension by checking if the Cronbacha coefficient is higher than that with other different structural variables correlation coefficient. If a factor has a high correlation coefficient that other factors and between, and Cronbacha coefficient the same structural variables is also higher than with others, then the questionnaire has high discriminant validity. Convergent validity refers to in the questions on the same aspect, statements are highly correlated. When all indicators load factor is higher than 0.5, it indicates that the questionnaire a high degree of convergence.

Table 6 Validity analysis resultsKMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy..945

Bartlett's Test of SphericityApprox. Chi-Square6223.153

df153

Sig.0.000

In the Principle Component Analysis across all ten statements, the KMO (>0.9) and Bartletts test (0.000) indicates the questionnaire is of good validity. If checking the validity by factors, Table 7 also shows the statements are valid within each facet of perceived risk.

Table 7 Validity test resutls for each facetKMOApprox. Chi-SquaredfSig.

Financial risk.717595.63.000

Performance risk.732718.93.000

Physical risk.699577.13.000

Social and psychological risk.737760.63.000

Time risk.708511.53.000

Privacy risk.709551.73.000

The significance are very strong so the validity of statements in each facet of perceived risk is also ensured.

4.4 Factor analysis

Factor analysis can be used to describe the variability of observed and correlated variables and build a series of unobserved variables. It can reduce the number of variables by their best combination and make further analysis possible and interpretable. In this study, for each aspect of perceived risk, three statements were used to describe them. When putting them all in regression, it is hard to decide whether the risk facet is important or not if the significance of statements are mixed. Therefore only one factor from each risk facet is extracted to represent that financial risk, performance risk and so on. In fact, in the factor analysis, it shows only one factor of the eigenvalue larger than 1. So one factor is enough to be picked out to represent the risk facet. Details of the factor analysis is shown in Table 8.

Table 8 Factor analysis resultsFacetItemExtractionComponent 1 loadingComponentEigenvalues% of Variance

Financial riskS10.7600.87212.24774.89

S20.7790.8832.42814.28

S30.7080.8423.32510.82

Performance riskS40.7520.86712.34878.27

S50.8040.8972.36612.20

S60.7920.8903.2869.53

Physical riskS70.6760.82212.21673.86

S80.7970.8932.47815.95

S90.7430.8623.30610.19

Social and psychological riskS100.8150.90312.38079.34

S110.7900.8892.34011.34

S120.7760.8813.2809.32

Time riskS130.6900.83112.17072.34

S140.7600.8722.46415.47

S150.7200.8493.36612.19

Privacy riskS160.7800.88312.20673.52

S170.7220.8502.45515.17

S180.7030.8393.33911.31

For all facets of perceived risk, the first component, Component 1 contributed to over 70% of the total variance of the three. So this was selected to be the specific risk taken forward to the next step of regression. Their loadings are listed in Table 7 as well. They are all larger than 0.5, which also means there is a high degree of convergence. The values of new factors are recorded as new variables for regression analysis.

4.5 Regression analysis

The main model of this dissertation is the overall risk model stated in Equation 1. The decomposed risk models cover uncertainty and severity separately. Their results are presented in this section.

4.5.1 Overall risk model

As described above, six new variables are computed to represent six dimensions of perceived risk. They are the independent variables in the linear regression and they all enter the model at the same time. The dependent variable is the product of Q5 and Q6 which is literally the average expense of online shopping in a month.

Table 9 Regression summary for the overall risk modelModel Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate

1.698.487.4811511.415

ANOVA

ModelSum of SquaresdfMean SquareFSig.

1Regression1103215041.0006183869173.50080.490.000

Residual1160463267.7685082284376.511

Total2263678308.769514

Coefficients

ModelUnstandardized CoefficientsStandardized CoefficientstSig.

BStd. ErrorBeta

1(Constant)1616.60866.60124.273.000

Financial risk-246.018110.284-.117-2.231.026

Performance risk-380.847115.006-.181-3.312.001

Physical risk-379.396108.774-.181-3.488.001

Social and psychological risk-287.86996.536-.137-2.982.003

Time risk-171.865116.095-.082-1.480.139

Privacy risk-295.083107.727-.141-2.739.006

In the model summary, the linear correlation coefficient R is close to 0.7 (0.698) and the F ratio of model fitting is significant, which means that the linear model is generally a good fit for the independent and dependent data.

In a summary of the variable coefficients, except time risk, all other risks, financial risk, performance risk, physical risk, social and psychological risk and privacy risk are significant at 95% level of confidence in the model. And their signs of coefficients are all negative, as expected, which means they are negatively associated with online shopping behaviour. The more risks consumer perceive, the less purchases they will make online.

4.5.2 Decomposed risk model

In the decomposed risk models, uncertainty is reflected in shopping frequency. The more likely it is for risk to occur, the less time people spend shopping online. Severity is reflected in shopping expenses. The more important a risk factor is, and the more severe its impact, the less money people will spend online.

Similarly, in line with the overall risk model, every three statements in the risk dimension are extracted to be one component by factor analysis. The details of the factors are described below.

Table 10 Factors for decomposed modelsFacetItemExtractionComponent 1loadingComponentEigenvalues% of Variance

Financial riskS1a.735.85712.10069.998

S2a.759.8712.55618.526

S3a.606.7783.34411.476

Performance riskS4a.778.88212.36178.707

S5a.819.9052.36312.108

S6a.764.8743.2769.185

Physical riskS7a.714.84512.25775.226

S8a.766.8752.41913.974

S9a.777.8813.32410.800

Social and psychological riskS10a.780.88312.33777.900

S11a.761.8722.35811.932

S12a.796.8923.30510.168

Time riskS13a.642.80212.16472.125

S14a.741.8612.51417.141

S15a.781.8843.32210.734

Privacy riskS16a.769.87712.27475.806

S17a.768.8762.38912.964

S18a.737.8593.33711.230

FacetItemExtractionComponent 1 loadingComponentEigenvalues% of Variance

Financial riskS1b.713.84511.92364.100

S2b.661.8132.64021.325

S3b.548.7403.43714.575

Performance riskS4b.683.82712.15071.663

S5b.728.8532.46515.495

S6b.738.8593.38512.841

Physical riskS7b.544.73811.89963.306

S8b.712.8442.65321.765

S9b.642.8023.44814.929

Social and psychological riskS10b.752.86712.23574.512

S11b.742.8622.39113.046

S12b.741.8613.37312.441

Time riskS13b.637.79811.92164.019

S14b.721.8492.64021.341

S15b.562.7503.43914.641

Privacy riskS16b.672.82011.95565.160

S17b.627.7922.55118.377

S18b.656.8103.49416.463

The scores for the six dimensions of perceived risk were input into linear regressions. In the first model, online shopping frequency was the dependent variable and scores of uncertainty are independent variables. The result of regression is displayed below.

Table 11 Regression summary for the frequency modelModel Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate

1.553.306.2982.985

ANOVA

ModelSum of SquaresdfMean SquareFSig.

1Regression1995.7716332.62937.331.000

Residual4526.4115088.910

Total6522.183514

Coefficients

ModelUnstandardized CoefficientsStandardized CoefficientstSig.

BStd. ErrorBeta

1(Constant)4.594.13234.927.000

Financial risk-.504.210-.141-2.402.017

Performance risk-.058.220-.016-.263.793

Physical risk-.307.229-.086-1.340.181

Social and psychological risk-.644.188-.181-3.422.001

Time risk.297.224.0841.327.185

Privacy risk-1.052.224-.295-4.704.000

In the second model, online shopping expense is the dependent variable and scores of severity are independent variables. The result of regression is displayed below.

Table 12 Regression summary for the expense modelModel Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate

1.577.333.325218.659

ANOVA

ModelSum of SquaresdfMean SquareFSig.

1Regression12138397.19962023066.20042.313.000

Residual24288435.51250847811.881

Total36426832.711514

Coefficients

ModelUnstandardized CoefficientsStandardized CoefficientstSig.

BStd. ErrorBeta

1(Constant)313.9099.63532.579.000

Financial risk-20.70515.079-.078-1.373.170

Performance risk-57.92916.152-.218-3.587.000

Physical risk-38.09615.504-.143-2.457.014

Social and psychological risk-41.40913.878-.156-2.984.003

Time risk-31.16715.907-.117-1.959.051

Privacy risk-1.50414.788-.006-.102.919

Although compared with the overall model, the correlation coefficient R of the decomposed models are reduced a lot (0.553 and 0.577), by the ANOVA test, the linear fit is still valid at 99% of significance. In the frequency model, only financial risk, social and psychological risk, and privacy risk are significantly and negatively correlated with online shopping frequency. In the expense model, there are performance risk, physical risk, social and psychological risk an