The Impact on Purchase Intention from Perceived Value Consumer Behavior Research on Online Shopping Robin R.P. Du 3/14/2009

CONTENTS ABSTRACT .................................................................................................................. 3 INTRODUCTION ....................................................................................................... 4 CONCEPTUAL FRAMEWORK AND HYPOTHESIS .......................................... 4 PERCEIVED BENEFITS ................................................................................................. 5 PERCEIVED RISK ......................................................................................................... 6 WORD OF MOUTH AND EXPERT RECOMMENDATION ................................................... 6 PERCEIVED VALUE ...................................................................................................... 7 PURCHASE INTENTION ................................................................................................ 8 THE RELATIONSHIP BETWEEN PERCEIVED BENEFITS AND PERCEIVED VALUE ............ 8 THE RELATIONSHIP BETWEEN PERCEIVED RISK AND PERCEIVED VALUE .................... 9 THE RELATIONSHIP BETWEEN PERCEIVED VALUE AND PURCHASE INTENTION ........... 9 THE MODERATING EFFECT OF INFORMATION TYPE ..................................................... 9 RESEARCH DESIGN AND METHOD .................................................................. 10 SURVEY INSTRUMENT DEVELOPMENT....................................................................... 10 MEASUREMENT ......................................................................................................... 13 RESULT ..................................................................................................................... 13 RESULT OF MODERATING EFFECT FROM INFORMATION TYPE.................................... 14 RESULT OF THE MEDIATING ROLE OF PERCEIVED VALUE ......................................... 15 DISCUSSION AND IMPLICATION ....................................................................... 16 LIMITATIONS .......................................................................................................... 17 CONCLUSION .......................................................................................................... 18 APPENDIX: SAMPLES QUESTIONS RELATED TO VARIABLES ................. 19 REFERENCES........................................................................................................... 20 ACKNOWLEDGEMENT ......................................................................................... 21

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The Impact of Perceived Value on Purchase Intension Ruiping Du (MBA) Directed by Prof. Hean Tat Keh

Abstract Near two decades of growth of online shopping that started from early 1990s, online shopping has got through its childhood, and become a mature business practice that is widely accepted today. The consumers’ attitude toward online shopping changed accordingly. This study reexamined the relationship among perceived risk, perceived benefits, perceived value, and purchase intention, and found, consistent to prior researches, perceived risk has negative impact on both perceived value and purchase intention; perceived benefit has positive effect on both perceived value and purchase intention. Although both perceived benefit and perceived risk are highly correlated to purchase intention, they mainly influence consumer’s purchase intention by influencing consumer’s perceived value, in other word, they influence consumer’s purchase intention in an indirect way. This study also tested the moderating effect of information type, and found that word-of-mouth is playing a more active role on reducing the negative impact from perceived risk on perceived value on online shopping than expert recommendation.

Keywords: perceived risk, perceived benefit, perceived value, purchase intention, expert comment, word of mouth.

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Introduction With the wider spread of popularity of internet, online shopping has grown from its conceptual stage in the early 1990s to today’s mature status. Today, numerous online transactions happen on daily basis. There are giant successful B2B website like alibaba.com, C2C like taobao.com and ebay.com, as well as other functional online shopping websites like hotel and air ticket booking, utility payment, etc. Perceived benefits, perceived risk, and perceived value all play critical role in consumer’s attitude toward product purchase. Many researches concerning these factors have been done in the traditional commerce context. Few have been done in the e-commerce environment. Also, with the spread of internet usage and online business practice, consumers’ attitude and perception toward e-commerce change constantly. Thus, a continuous empirical research on these factors is imperative for managers and researchers to understand consumer behaviors. Nowadays, internet has evolved into a mature and popular medium. Information and knowledge is much easier for access. Today, a savvy buyer may check product review online before he or she makes a real purchase decision. This is especially true for online buyers. Typically, there are two broader categories of online information concerning particular products - user comments (word-of-mouth) and expert recommendation. Previous researches showed these two types of information have different impact on consumer’s attitude toward purchase intention and perceived value in certain industry. In this research, we want to check what a role of either word-of-mouth (WOM) or expert recommendation (ER) is playing toward general online shopping consumers. The objectives of this research were to: 1. Qualitatively investigate the relationship among perceived benefits, perceived risk, perceived value, and purchase intention. 2. Qualitatively investigate the impact on perceived benefits, perceived risk, and perceived value from word-of-mouth (WOM) and expert recommendation (ER). 3. Identify and examine rationale behind the findings and implication to managers.

Conceptual Framework and Hypothesis Based on previous researches, we developed the model as illustrated in Figure 1, where perceived benefits and perceived risk directly influence consumer’s perceived value, and influence purchase intention in an indirect way (using perceived value as 4|Page

an mediator). Information type (two broad categories in our research: word-of-mouth (WOM) and expert recommendation (ER)) influences perceived value in a moderate way by influencing perceived benefits and perceived risk. Figure 1: Conceptual Model WOM versus

Perceived Benefits Perceived Value

Purchase Intention

Perceived Risk

Perceived Benefits Researches on traditional shopping formats show that personal determinants of shopping are influenced by functional and nonfunctional motives (Sheth 1983). Functional motives are motives associating with utilitarian functions such as convenience, variety, price etc. Nonfunctional motives are those associating with social and emotional needs for enjoyable and interesting shopping experience (Sandra Forsythe, Chuanlan Liu 2006). Because online shopping is different from traditional shopping formats, for instance, a patron who buys merchandise via physically visiting the shop may value some factors like the touching and verbal bargaining in shop more than online shopping, in other word, buying a same merchandise via visiting shop or online shopping can mean different perceived benefits for him or her. The research conducted by Childerset in 2001 showed that both functional and non-functional (hedonic) was important predictors of attitude toward online shopping but in a different weight compared to traditional shopping formats. Although many researches have been done concerning the impact of perceived benefit toward perceived value, few empirical researches have been focusing on the interaction perceived benefits and perceived value in online shopping. Here, we define perceived benefit is a consumer’s assessment of benefits by acquiring some goods (or services). In our research, we measure perceived benefits in four dimensions: convenience, selection, ease, and hedonic.

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Perceived Risk Since Bauer (1960) stimulated the marketing research to explicitly incorporate perceived risk into consumer research. Many researches have been conducted over the past 40 years. The theory or perceived risk also developed into its adulthood from infancy. In Bauer’s definition of research (1960), there were only two dimensions: uncertainty and adverse consequences. Since then, many researches focused on its uncertainty dimension. In Bauer’s original definition, the adverse consequence is not explicitly defined. One decade later Taylor (1974) defined adverse consequences as "importance of loss". Bloch and Richins (1983) have replaced the adverse consequences component with a concept called "instrumental importance." In addition to its principle two-dimensional structure, many researches proposed that the concept of perceived risk is related to various types of losses. The most popular and highly quoted definition of perceived risk as a two dimension (importance and probability of loss), multifaceted (performance, social, psychological, physical, financial loss, etc) developed by a group of researchers (Peter and Tarpey 1975, Peter and Ryan 1976, Vincent and Zikmund 1976, Bearden and Mason 1978, and Dowling 1985). Social risk reflects the disappointment and embarrassment before family or friends as a result of the poor choice made (Hean Tat Keh and Jin Sun 2008). Psychological risk is the harm to the consumer ego that a poor choice produces (Jacoby and Kaplan 1972). Financial risk pertains to a customer’s financial loss in the case of poor service choice (Stone and Gronhaug 1993). Performance risk is defined as the loss incurred when the service does not perform as expected (Stone and Gronhaug 1993). In our research, the main goal was to find out the relationship between perceived risk and other parameters (perceived value, purchase intention, etc). We didn’t focus on which dimension of perceived risk is influencing the other parameters. Instead, we take perceived risk as a whole. Word of Mouth and Expert Recommendation Unlike advertisement, both word-of-mouth (WOM) and expert recommendation (ER) are third-party information (Chen and Xie 2005). Most online shopping websites offer user comments or user ranking function in their websites. Online buyers post on websites before or after their purchase is word-of-mouth by nature. They usually cover a big range of facts such as product’s design, functions, weight, seller’s service, etc. Unlike user comments, expert recommendation is comment from a more professional perspective. Experts usually follow a particular framework to give recommendation concerning to a certain type of products.

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Previous research on movie industry shows that word-of-mouth has more impact on infrequent moviegoers than movie critics (i.e. expert recommendation), whereas movie critics have more impact on frequent moviegoers than infrequent ones (Anindita Chakravarty 2008). It offers a perspective of impact of online word-of-mouth and expert recommendation in a certain industry. Besides this, the effect of consumer communication on product evaluation, adoption and diffusion are frequently conducted by researchers (e.g., Mahajan, Muller, and Wind 2000, Awad, Dellarocas, and Zhang 2004). Our research did not go deep on the effect of consumer communication, e.g., word-of-mouth and expert recommendation. Instead, we examine impact the word-of-month and expert recommendation toward online shopping to see what roles they are playing on influence perceived benefits, perceived risk, and perceived value. Thus, here, we adapted a broader definition of word-of-mouth and expert recommendation. We regard all the comments made by the users online or suggestions given by friends or family members as word-of-mouth. For expert recommendation, we take all the comments, review, or evaluation reports made by experts on internet or other media into the category of expert recommendation toward online shopping. Perceived Value Perceived value, a strategic imperative for producers and retailers in the 1990s, will be of continuing importance into the twenty-first century (Vantrappen, 1992; Woodruff, 1997; Forester, 1999). With the development of world economy and globalization, many researcher and business practitioners regards value as the key to long-term success. Some even argue that “the only thing that matters in the new world of quality is delivering customer value” (Albrecht 1992). Hence, it’s critical for managers to understand what customer’s value and where they should focus on to achieve market advantage (Woodruff, 1997). The most common definition of value is as a trade-off between quality and price (Chain Store Age, 1985; Cravens, Holland, Lamb & Moncrieff, 1988; Monroe, 1990). Other authors have been arguing that viewing value as a trade-off between quality and price is too simplistic. Based on different definition and argument, different measurements of perceived value have been developed. In 1994, Babin, Darden and Griffin developed a specific measurement of shopping value that includes utilitarian and hedonic components, while Richins (1994) created a ‘possession rating scale.’ Other broader theoretical framework for measurement like measuring perceived value in five dimensions which 7|Page

includes social, emotional, functional, epistemic and conditional value (Sheth, Newman and Gross 1991). In our research, our focus is to test the relationship among perceived value, perceived risk, perceived benefits, and purchase intention. Thus, we didn’t divide perceived value into multifaceted like other authors did as mentioned above. Instead, we adapted the definition of perceived value as Zeithaml (1988) suggested. Perceive value is a consumer’s overall assessment of the utility of a product (or service) based on perceptions of what is received and what is given. Purchase Intention It is not difficult for consumers to understand what purchase intention is. Hence, we adapted the widely accepted definition of purchase intention, which is the plan to buy a goods or services in the future. The Relationship Between Perceived Benefits and Perceived Value Compared to traditional shopping experience, online shopping has its unique advantages such as browsing product without leaving home, comparing price of same product in a short time, etc. However, it has its own drawback too. It cannot offer buyers physical touching or testing of product. According to previous research, consumer’s buying behavior is driven by functional motives as well nonfunctional motives. In a functional dimension, online shopping gives consumer convenience - buying without leaving home, wide selection browsing various products in a few mouse clicks, ease – paying for product easily. In a nonfunctional dimension, it offers consumer hedonic shopping experience like what consumer experiences in a traditional shopping experience. Naturally, we link the perceived benefits with perceived value in a positive way. Namely, the greater a consumers perceive for benefits, the greater he or she will perceive the product value. Hence, we develop the formal hypothesis as stated below. H1: Perceived benefit is positively related to perceived value.

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The Relationship Between Perceived Risk and Perceived Value Previous research has shows that perceived risk is negatively related to perceived value (Sweeney, Soutar, and Johnson 1999). Although the research was not conducted in e-commerce environment, we believe the same relationship will demonstrate on online shopping as well. Thus, we reexamine the relationship again. Formally, the hypothesis is stated as follows: H2: Perceived value is negatively related to perceived value. The Relationship Between Perceived Value and Purchase Intention It is nature to think that the higher the perceived value is, the greater the purchase intention will be, although a purchase intention does not necessarily lead to a real deal. Here, we state the hypothesis between perceived value and purchase intention. H3: Perceived Value is positively related to Purchase Intention. The Moderating Effect of Information Type Information plays a critical role on consumer’s purchasing decision making process. As we described in the previous paragraph, six facets (physical risk, psychological risk, financial risk, performance risk, time risk, and social risk) of perceived risk, each of them can be caused by not being well informed although sometime it happens in a passive way like there’s no information available. Say performance risk, it is nature to think that the performance risk will be reduced when consumer is well informed about the performance of the product he or she intends to buy. When consumer knows the data about the track record of the product, or he or she gets information through friends, someone who bought similar product before or even online article of professional comments about the product’s performance, the consumer usually will feel more confident when he or she put the order. Other facets of perceived risk have similar connection to information. For perceived benefit, we think the information is also playing a related role to it. By accessing information about the goods or service he or she is going to buy, consumer can know better about the goods or service. Simultaneously, consumer knows more potential benefits he or she can gain from the goods or service he or she is going to purchase. Therefore, we think the perceived benefit will increase when consumer is 9|Page

better informed. There are numerous ways for consumer to get information about the goods or service. In this study, to simplify the model, we put information type about online shopping into two broad categories: word-of mouth (WOM) and expert recommendation (ER). The reason to classify information type is to investigate whether different information resources are playing different role or having different effect on consumer’s perceived benefit, perceived risk, furthermore, having different effect on perceived value through the effect on these two factors. Hence, we developed our formal hypothesis of effect from information type as they are stated as follows. H4a: The better the online shopper is informed through word-of-mouth (WOM), the greater the likelihood that perceived benefit will lead to greater perceived value. H4b: The better the online shopper is informed through expert recommendation (ER), the greater the likelihood that perceived benefit will lead to greater perceived value. H5a: The better the online shopper is informed through word-of-mouth (WOM), the greater the likelihood that it will reduce the negative impact created by perceived risk. H5b: The better the online shopper is informed through expert recommendation (ER), the greater the likelihood that it will reduce the negative impact created by perceived risk. H6: Both word-of-mouth and expert recommendation have equal likelihood to influence perceived value by putting impact on perceived benefit or perceived risk.

Research Design and Method Survey Instrument Development The reasons we choose to conduct the research on online shopping are mainly out of four, a)the relationship between perceived risk and perceived value have been done by many researchers in traditional way of purchasing; b)online shopping involves more risk compared to traditional way of visiting a store such as advance payment before checking the goods or services; c)most of online buyer are experienced internet surfers, user comments online or professional evaluation of product are more accessible to them; d)with the wider spread of internet using, people’s psyche and attitude toward online shopping changes accordingly. A continuous follow of online 10 | P a g e

consumers’ behaviors is necessary. We conducted survey in Singapore. Data were collected from only those who are holding a Singaporean citizenship and having been lived in Singapore for over three years. Singapore is a quite diversified country in terms of races, and still remains its uniqueness. The population of Singaporean mainly consists of ethnic Chinese, Malay, and Indian. Compared to other developed country like US, UK, and developing country like China, the online shopping activities in Singapore is not highly active. The questionnaires were mainly dispatched and collected in public spots including Singapore Changi International Airport, Clementi MRT station, and campus of National University of Singapore. Some questionnaires are collected through internet. We set up an online questionnaire form on Google docs website, and sent the link to local Singaporean who we know and asked them to forward the link to their friends and family members. Before giving the questionnaire to the respondents, we asked them whether they have conducted online shopping before, and the questionnaires were only given to those who have done online shopping before. On average, it takes a respondent around 15 minutes to fill-in a printed version of questionnaire, and 20 minutes to fill-in an online questionnaire. After filtering out invalid and incomplete questionnaires, our final sample size consists of 132 fully filled in surveys. The summarized sample statistics shows as follows.

(This space is intentionally left blank)

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Table 1: Sample Statistics Demographic Information Gender Male Female Age 20 or less 21-30 31-40 41-50 51-60 61 or more Degree of education: High school Bachelor Master Doctor Occupation Full time job Part time job Student Monthly income 0-1999 2000-3999 4000-5999 6000-7999 10000 and more Shopping Website Taobao.com eBay.com Amazon.com Other Merchandise Bought Online Other Book Clothing Toy Cosmetic Digital Products Home Appliances Office Supplies Convenience Goods Tickets

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Samples (132) Frequency Percentage 62 70

47.0 53.0

22 86 13 7 3 1

16.7 65.2 9.8 5.3 2.3 0.8

29 64 33 6

22 48.5 25 4.5

45 8 79

34.1 6.1 59.8

82 33 13 2 2

62.1 25 9.8 1.5 1.5

3 74 20 35

2.3 56.1 15.2 26.5

14 50 22 6 4 23 3 2 2 6

10.6 37.9 16.7 4.5 3.0 17.4 2.3 1.5 1.5 4.5

Measurement For the measurement of all the variables, which are Perceived Benefits, Perceived Risk, Word-of-Mouth and Expert Recommendation, Perceived Value, and Purchase Intention, we adapted the seven-point scale (1 = “strongly disagree, very unlikely, or very useless,” 7 = “strongly agree, very likely, or very useful.”). Perceived Benefit. In this research, perceived benefit includes four dimensions: convenience, selection, ease, and hedonic. Perceived Risk. In this research, we did not the multi-dimension of perceived risk. Instead, we measured the perceived risk as a whole. The questionnaire is designed to direct respondents to evaluate their overall perceived risk. WOM and ER. In this research, we didn’t design dedicated questions to test the source of information type to see whether it is from word of mouth or from expert recommendation. We allocate one question with six sub-questions, which is Q4 in the questionnaire (See Appendix I) to use as the proxy of information type. We use the difference between the average scale points to calculate the impact of word-of-mouth and expert recommendation. (𝑄𝑄41 + 𝑄𝑄42 + 𝑄𝑄43) (𝑄𝑄44 + 𝑄𝑄45 + 𝑄𝑄46) − = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑜𝑜𝑜𝑜 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 3 3 Where 𝑄𝑄4𝑖𝑖 (𝑖𝑖 = 1,2,3,4,5,6) represents the sub sequential questions under Q4. If the value of Effect of Info. Type is greater than zero, the information type relies on word of mouth (WOM), otherwise, it relies on expert recommendation (ER). Perceived Value. In this research, we didn’t measure perceived value in the 19-item PERVAL measurement (Jillian C. Sweeneya and Geoffrey N. Soutarb 2001). Instead, we measure perceived value as a whole. The questions are designed to direct the respondents to show their perceived value in a seven-point scale. Purchase Intention. Seven-point scales question are designed to measure the respondents’ purchase intention similar to the measurement of perceived value. Result To make sure the survey samples we collected are valid and effective. We conducted the Cronbach’s α analysis to test the reliability. In the questionnaire, different questions are allocated to test different dimension of 13 | P a g e

these variables - Perceived Benefit, Perceived Risk, Perceived Value, and Purchase Intention, (See Appendix A: sample questions related to variables). Table 2: Survey's Reliability Test Variable

Facet

Purchase Intention Perceived Benefit Convenience Selection Ease Hedonic Perceived Value Perceived Risk

Denotation

Cronbach's α

PI1 - PI3

0.903

PB1-PB4 PB5-PB8 PB9-PB12 PB13-PB16 PV17-PV20 OR1, OR2, OR4

0.808 0.817 0.824 0.796 0.840 0.714

The reliability test results tell us the parameters we set up in the questionnaire to measure is reliable. Using regression model, we firstly tested the relationship between perceived benefits and perceived value, and the relationship between perceived risk and perceived value. The result demonstrated a significant positive relationship between perceived benefits (β = .599, p < .001) and perceived value, which verified our hypothesis H1. The greater the consumer perceives the goods or service will bring to him or her, the greater value he or she perceives about the goods or service. The perceived risk (β = -.151, p < .05) has a negative effect on perceived value, which verified our hypothesis H2. The greater risk the consumer thinks he or she will get involved in when doing an online purchase, the lower value he or she perceive toward the goods or service. Using same regression model, we then tested the relationship between perceived value and purchase intention. The result proved our hypothesis – perceived value (β = .536, p < .001) is positively related to purchase intention. The greater the consumer think the perceived value is, the greater chance he or she intend to buy the goods or service online. Figure 2 shows the tested structural relationship that we developed from the conceptual model. Result of Moderating Effect from Information Type To test the different effects from information type, word-of-mouth (WOM) versus expert recommendation (ER), we induced the information type as a dummy variable, which is either equals “0” or “1”. “1” stands for the information from WOM, whereas “0” stands for the information from ER. We also introduced the interaction variable PB2IS, denoting the interaction between perceived benefit and information type; 14 | P a g e

PR2IS, denoting the interaction between perceived risk and information type. Based on these variables and those we induced in pervious paragraph, we develop the regression model. The result shows both information types, WOM and ER, are playing a significant role on increasing perceived value. However, they influence perceived value through different means. Expert recommendation positively influence perceived value by positively influence perceived benefit (β = .752, p < .001), whereas word-of-mouth positively influence perceived value by reducing the negative effect caused by perceived risk (β = .862, p < .05). Therefore, our hypothesis H4b and H5a are supported, whereas hypothesis, H4a, and H5b, are rejected. Surprisingly, expert recommendation increases online shopper’s perceived risk (β = -.328, p < .05). The result is not significant to show impact to perceived value by impacting perceived benefit. Therefore, our hypothesis H6 is rejected. Table 3: Result from Information Type Model Regression Unstandardized Coefficients Model 1

Std. Error

B

Standardized Coefficients Beta

t

Sig.

B

Std. Error

2.548

0.012

(Constant) 2.344

0.92

PB

0.814

0.116

0.725

7.032

0.000

PR

-0.371

0.15

-0.328

-2.464

0.015

IS0

-0.336

1.135

-0.16

-0.296

0.768

PB2IS

-0.206

0.158

-0.497

-1.303

0.195

PR2IS

0.399

0.177

0.862

2.251

0.027

Result of The Mediating Role of Perceived Value To test the mediation effect of perceived value between perceived benefit, perceived risk, and purchase intention. We built direct path between perceived benefit and purchase intention as well as direct path between perceived risk and purchase intention. Using the method introduced by Baron and Kenny in 1986, we conclude that although both perceived benefit (β = .381, p < .001) and perceived risk (β = -.182, p < .05) have highly related to consumer’s purchase intention. The effect through 15 | P a g e

perceived value (β = .536, p < .001) as a mediating variable is more dominant that the direct effect. Namely, perceived benefit and perceived risk influence consumer’s purchase intention by influencing consumer’s perceived value. Figure 2: Relationship Structure Information Type ER

0.725*** Perceived Benefits

vs.

WOM

0.862* 0.381***

0.599*** Perceived Value 0.536***

-0.151* Perceived Risk

Note: *p<.05, ***p<.001

Purchase Intention

-0.182* or or

Supported Relationship Unsupported Relationship

Discussion and Implication For decades of rapid growth in personal computer and information technology, people are not unacquainted with e-commerce and online shopping any more. Compared to traditional sales through a conventional distribution channel or retail outlet, online shopping has its unique advantages to a seller’s standpoint such as low overhead cost, low inventory holding cost, etc. It also provides shoppers advantage with unique flavor such as shopping without going out, fast attributes or price comparison, etc. It has becoming an unstopping trend of sales form. Therefore, it is imperative for business practitioners and marketing researchers to understand the psyche of online shoppers. Although our study prove the conventional relationship between perceived risk and perceived value, i.e. perceived risk negatively influence perceived value, researchers have also found the relationship may vary under different culture context (e.g. China) (Hean Tat Keh and Jin Sun 2008). Therefore, when managers apply these findings in 16 | P a g e

real business practices, it is imperative for them to realize the potential difference under different circumstances. When we developed the hypothesis of influence from information type, we assume that word-of-mouth would have same influence as expert recommendation does. On one hand, our findings point out word-of-mouth has stronger influence than expert recommendation. The possible reason can be online shoppers concern that expert recommendation could be soft-advertisement with sponsorship from sellers or manufacturer. Therefore, people trade the word-of-mouth, e.g., online comment from user, friends, or pervious shoppers, more seriously than they do to expert recommendation when it concerns to risk. On the other hand, our findings shows that although expert recommendation increase online shopper’s perceived value while it hinders perceived risk too although it does not in the same manner of significance. When managers build their online selling practices, they should utilize our findings here. Namely, try to influence online shoppers through word-of-mouth (WOM). For instance, they can set up online user comment mechanism. Each time when a deal is closed, they give buyers chance to comment on the goods or service they purchased. Similarly, they can set up ranking system too to let buyers to give ranking for buyer’s practice as well as attributes of products. Managers should also try to avoid the negative influence coming from expert recommendation, for instance, avoiding only put positive expert recommendation relating to their own products. In a nut shell, most conventional wisdom on perceived risk, perceived benefits, and perceived value apply to online shopping context, whereas different information type may have different impact on these parameters.

Limitations Our analysis of this empirical study shows general relationship among perceived benefits, perceived value, perceived risk, and purchasing intention as well as the moderating effect of information type. However, as industry varies and the different dimension of all these factors may have different impact to a specific industry, it necessary to examine the dimensions of these variables in different industry. In our questionnaire, although we designed the questions to measure different facets of each variable like perceived benefits and perceived risk. It’s not designed to test the relationship among all these facets. Previous research showed the effect of perceived risk may have different effect in a cross-culture context, for instance, perceived risk has different effect on customer 17 | P a g e

satisfaction in different culture background (Hean Tat Keh 2008). Thus, we hope more empirical studies to be conducted in a cross-culture context. Previous research has shown different information type may have different impact to a segmentation of consumers in a specific industry (Anindita Chakravarty 2008). Our model for moderating effect of information type is too simplistic to examine this sort of relation.

Conclusion This study replicates the findings of relationship among perceived benefits, perceived risk, perceived value, and purchase intention in online shopping context. We found that findings conducted in the context of traditional shopping apply for online context too. Partially different from previous research, we investigate the influence on perceived value from information type, and we found that both information types (WOM vs. ER) have positive impact on perceived value but in a different way. To summarize, our empirical study found the evidence of the following. 1. Perceived benefits positively influence perceived value. 2. Perceived risk negatively influences perceived value. 3. Perceived value positively linked to purchase intention. 4. Although both perceived benefits and perceived risk influence purchase in a positive and negative way respectively, the influence from perceived value as a mediator is more dominant. 5. Although both information types, i.e. word-of-mouth and expert recommendation have positive impact on perceived value but in a different way. Expert recommendation positively influence perceived value by positively influence perceived benefits while word-of-mouth positively influence perceived value by negatively influence perceived risk.

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Appendix: Samples questions related to variables “Strongly Disagree”  “Strongly Agree” Purchase Intention

How likely do you want to consider this Website again? (PI1) How likely do you recommend this Website to your friends? (PI2) How likely do you want to use this Website again? (PI3)

ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο

Perceived Benefits Convenience

Using this Website, Can shop in privacy of home(PB1) I don’t have to leave home (PB2) Can shop whenever I want (PB3) Can save the effort of visiting stores (PB4)

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

ο ο ο ο

Selection

Product Items from everywhere are available (PB5) Can get good product information online (PB6) Broader selection of products (PB7) Access to many brands and retailers (PB8) Ease

Don’t have to wait to be served (PB9) No hassles (PB10) Not embarrassed if you don’t buy (PB11) No busy signal (PB12) Hedonic

To try new experience (PB13) Exciting to receive a package (PB14) Can buy on impulse in response to ads (PB15) Can custom design products (PB16) Perceived Value

Products purchased at this Web site are valuable for money. (PV17) Using this Website, Products purchased at this Web site are considered to be a good buy (PV18) You get what you pay for at this Web site (PV19) Products purchased at this Web site are worth the money paid (PV20) Perceived Risk Choose a website is: "Not at all risky  Extremely risky" (OR1) Choose a website is: "Not at all concerned  Highly concerned" (OR2) Choose a website is: "Not at all worried  Very worried" (OR4)

ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο ο

Note: The questions in questionnaire are not listed in the sequence or category listed above. Questions listed into category relating to variables are for analysis only.

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Acknowledgement It was a both exhilarating and painful process throughout the three-mouth thesis preparation and writing. I am not sure whether this will be my once-a-life-time experience. Nonetheless, the journey was wonderful and surely worth a lifetime memory. At Singapore Changi International Airport, when I was dispatching and collecting data, an Islamic lady wrote a note on the back page after filling-in the questionnaire, “I appreciate your hard working, hope for your success in the courses, and pray for your future success in the business world.” It injected energy to me instantly after the long day work. I thank this lady for her sincere generosity. I thank the scholar I met in NUS HSMML library who taught me how a research work should be done, and showed me the way how he conducted his own work. I thank all the respondents who I met at airport, MRT station, and NUS (National University of Singapore), and those who fill in my questionnaire though internet. I thank my Korean friend, Hyogil Kim, who helped me build up the online questionnaire. Most importantly, I thank my mentor, Professor Hean Tat Keh, and my tutor, doctoral candidate Ji Wenbo. It is not easy for a professor to mentor a student when the student is physically in another country. However, Professor Hean Tat Keh offered continuous 21 | P a g e

help and support via email even when he was in Malaysia for the Chinese Lunar Near holidays. Wenbo offered me a lot of help on questionnaire before and after data analysis. The thesis couldn’t be done without their constant mentoring and suggestions.

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