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In-depth Study on the Influence of Internet Perceived Risk on the

E-WOM

GUO Guoqing, YAO Yanan,YU Lianzhao

Business School, Renmin University of China, Beijing, P.R.China, 100872

Abstract:This empirical study explores the relationship between internet perceived risk and E-WOM.In this study, internet perceive risk is divided into four dimensions based on exsited literrature which are

internet core-service risk, internet associated risk, internet privacy risk and internet fake risk, andE-WOM is studied from information releasing and opinion seeking perspective as an input variabe ofconsumer purchasing progress. Finally, the study found that all four internets perceived risks have

posive effect on E-WOM. Moreover, the high perceived risk will lead to frequent information releasing,information searching is still the most important measure for consumers to reduce their internet

perceived risks and consumers with high perceived risk are more prone to search for E-word-of-mouthinformation actively.Keywords:Internet, Perceived risk, E-WOM

1 Introduction

With the development of internet, E-WOM (Electronic Word-of-mouth) is becoming a universal

message when consumenrs make their purchase decisions. Consumers communicate with each other

online before making their decisions can reduce their perceived risk. This study will explore the

relationship model between internet perceived risk and E-WOM.

2 Theory Background

2.1Internet Perceived Risk

Perceived Risk was firstly originated from psychology research domain, which is defined as any

consumer action will lead to unexpected result which may be unpleasant, so consumer behavior is a kind

of risk bearing in itself. Forsyth and Boshi

2003

[1]defined perceived risk in the internet environment as

the consumer subjective expected lose when they experience online shopping. In this study, we define

the internet perceived risk consumer perceived loses before making purchase decision which are led

by product/service, internet and online shops during purchase and use process.

Information searching is the most effective method to reduce the perceived risk

Sheth and Venkatesan,

1968

[2], Boulding and Kirmani

1993

[3]thought that consumers will reduce their perceived risks by

searching for formal or informal information related to products, including brand reputation, free use,

buying products with high quality image or repeat purchase.

As for dimensions of internet perceived risk, Miyazaki 2001 [4]researched into the influence of privacyrisk and security risk on consumer behavior online. Forsyth and Boshi (2003)

[1] evaluated internet

perceived risk in terms of 4 dimensions: Financial risk, performance risk, psychology risk and time risk.

This study adopts the dimension of internet perceived risk developed by Chinese scholar Dong Dahai

2005

[5 ]which is composed of internet core-service risk, internet associated risk, internet privacy risk

and internet fake risk.

2.2 E-WOM

Word-of-mouth communication is an oral, person-to-person communication between a receiver and a

communicator whom the receiver perceives as non-commercial regarding a brand, a product, or a

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service (Arndt, 1967). [6]

However, with the development of internet, more and more consumers are

communicating with each other and sharing their opinions online, and E-WOM

Electronic Word of

Mouth

is emerging accordingly. On the basis of definition of Hennig-Thurau

2004

[7]

, we defineE-wom as the two-way communication related to products, enterprises and newsmakers online.

Compared to the information provided by enterprises, E-WOM on web forum has much more

significant impact on consumer behavior Bickart, Schindler 2001[8]

. This study takes E-WOM as

input variable, which refers to information searching online before making purchase decision including

information releasingand opinion seeking.

3 Hypotheses

The more frequent WOM behavior is, the higher perceived risk is

Arndt,1967

. Consumers with high

perceived risk will search for word-of-mouth information actively. A few scholars[4] [7] [8]

also found that

reducing perceived risk is the main reason for E-WOM communication. Moreover, consumers will also

release word-of-mouth information motivated by altruism and self-enhancement. Consumers willrelease information and share them with others while searching information. Therefore, internet

perceived risk has positive effect on information releasing. We suppose that:

H

Internet perceived risk has positive effect on E-WOM.

In order to explore the effects of 4 internet perceived risk dimensions on information releasing and

opinion seeking online, we make further hypotheses as follows:

H1: Internet core-service risk has positive effect on information releasing online.

H2: Internet core-service risk has positive effect on opinion seeking online

H3: Internet associated risk has positive effect on information releasing online.

H4: Internet associated risk has positive effect on opinion seeking online.

H5: Internet privacy risk has positive effect on information releasing online.

H6: Internet privacy risk has positive effect on opinion seeking online

H7: Internet fake risk has positive effect on information releasing online.

H8: Internet fake risk has positive effect on opinion seeking online

Figure 1. Research Model

4 Methodologies and Analysis

4.1 Sample

Information

releasing online(IR)

Opinion seeking

online(OS)

Internet fake

risk(FR)

Internet

privacy risk(PR)

Internetassociated risk(AR)

Internet core servicerisk (CR) H1 +

H2

+

H4

+

H5 +

H6

+

H7

+

H3

+

H8

+

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Data for this study are collected through online survey from consumers who bought their mobiles online.

318 questionnaires are handed out and 288 valid ones were received. The gender proportion is balanced

with 57.99% males and 42.01% females. 90.28% samples are below 35 years old, most of which arestudents with high education.

4.2 Latent variables and measurement

This study employes the scale of perceived risk posed by Chines scholar Da-hai Dong (2005) which is

composed of 19 items and measured in terms of Likert which rages from 1 (entirely disagree) to 7 (full

agree).

Table1 Latent variables, operational definations and scales

Concepts Dimensions Definitions Scales

Internet

core service

risk

Consumer

perceived risk

related to net

shops and web

sites.

The product information online are exaggeratedand even false

The refund of shopping online may beinconvenient.

The guarantee of shopping online may beinconvenient.

Maybe I cant receive the delivery. The net shop may be inexistent Maybe there is damage to goods during

delivery process.

I cant get enough product information due tothe lack of face-to-face interaction with

salesman.

There may be a big gap between the real goodsand expected goods.

Internet

associated

risk

Consumer

perceived possible

risk related

shopping online.

The payment failure online may lead to moneylost.

Searching for information online may betime-consuming due to the massive

information.

It may be a long time to receive the goods. The hacker maybe steal the credit card

information which lead to money lost

Taking the delivery by myself may betime-consuming.

Internet

privacy risk

Consumer

perceived risk

related to

personnel

information

leakage.

My shopping experience and habit may beanalyzed by online shop.

The net shop maybe connects with me withoutmy permission.

My personnel information may be leaked.

Internet perceived

risk(PR)

Internet

fake risk

Consumer

perceived possible

risk related to the

fake.

The return of goods may be time-consuming. The goods ordered online may be fake. There may be metal pressure when shopping

online

E-WOMInformation

releasing

Consumer sends

message or gives

I know mobiles very well; many people believethat I am the opinion leader.

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online advice to others

online

My friends think my opinion is the reliableinformation source when they decide to buy

mobiles online. I talked about mobiles with my friends in last 6

months.

Compared to others, many people are moreprone to my opinion when they ask for

suggestions about mobiles online.

I think that my friends rely on my opiniononline.

I like to talk about mobiles with my friendswhen I chat online.

I want to influence others view on mobilesthrough internet.

Opinion

seeking

online

Consumer

searches for

opinion or advice

from others.

Im prone to ask advices from others beforebuying mobiles

Ill search for the latest information aboutmobiles before buying mobiles.

Ill ask for suggestions about mobiles from myfriends online.

Ill ask for suggestions throng BBS, QQ, MSNand EMAIL before buying mobiles.

Ill be at ease if I ask for suggestions onlinebefore buying mobile

Ill ask others to provide information aboutmobiles for me.

I like to search for negative comment onmobiles before making purchasing decision.

I like to search for positive comment onmobiles before making purchasing decision.

Resource: summarized by authors

4.3 Reliability and validity

IPR and E-WOM scales have quite high reliabilities. The result of EFA shows that after deleting CR 4

and CR 6, the scale of perceived risk has high convergent validity and the 4 perceived risk factors

cumulative contribution of variance account to 87.313%. The resuls of CFA show that the scales of IPR

and E-WOM have high model fitting and validity.

Table 2 Reliability and vlidity of the variables

Variable Reliability RMSEA CFI NFIIPR =0.785 0.072 0.885 0.901

E-WOM =0.929 0.066 0.879 0.901

4.4 The effect of perceived risk online on E-WOM

We employ Amos to examine the relationship between internet perceived risk and E-WOM and the

result is as follow:Table 3 SEM result

Path coefficient Level of sig.

CR IR 0.27 0.012

AR

IR 0.32 0.010

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PR IR 0.36 0.000

FR

IR 0.45 0.000

CR

OS 0.36 0.000AR

OS 0.45 0.000

PR OS 0.25 0.014

FR

OS 0.39 0.000

We can see from Table 3 and Table 4 that RMSEA is 0.085>0.08, NFI, RFI, IFI, TLI and CFI are all

near to 1, so the fitting of model is well and all hypotheses are supported. Therefore, the perceived risk

online which is composed of internert core service risk, intenet associated risk, internet privacy risk and

internet fake risk has positive effect on E-WOM which is composed of information releasing online and

opinion seeking online.

Table 4 The path coefficient and fitting index of the model

df 2/df p-level RMSEA NFI RFI IFI TLI CFI

807.161 369 2.187 0.000 0.085 0.892 0.868 0.841 0.791 0.818

5 Conclusion

We can see from this study that internet perceived risk has positive effect on E-WOM.

As far as opinion leader is concerned, the high perceived risk will lead to frequent information releasing,

which is in accord with the study of Hennig-Thurau 2004 .[7]

However, the content of information

under the high perceived risk is still needed to a further research.

Information searching is the primary and important measure for consumers to reduce their perceived

risks and consumers with high perceived risk are more inclined to search for E-word-of-mouth

information actively.

Acknowledgments:Supported by projects of National Science Foundation of China (Grant No.70772090, 70972133)

References

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[2]. Sheth J N, Venkatesan M. Risk-reduction Process in Repetitive Consumer Behavior. MarketingResearch, 1968 (5), 307-310

[3]. Boulding, William & Kirmani Amna. A consumer-side examination of signaling theory. Journal ofConsumer Research, 1993(20-1), 111-123.

[4]. Miyazaki, Anthony D, Fernandez, Ana. Consumer Perceptions of Privacy and Security Risksfor Online Shopping. Journal of Consumer Affairs, 2001 (35-1), 27-44.

[5]. Dong Dahai, Li Guanghui, Yang Yi. Research of the Perceived Risk Facets by Consumers inInternet Shopping. Chinese Journal of Management, 2005

1

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In Chinese

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[7]. Hennig-Thurau, Thorsten. Kevin P. Gwinner, Gianfranco Walsh, and Dwayne D. Gremler.Electronic Word-Of-Mouth Via Consumer-Opinion Platforms: What Motivates Consumers to

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