Consumer Trust in Electronic Commerce the Impact of Electronic Commerce Assurance on Consumers Purch

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PrimaVera Working Paper Series

PrimaVera Working Paper 2000-24

Consumer Trust in Electronic Commerce:

The Impact of Electronic Commerce Assurance onConsumers' Purchasing Likelihood and EC Risk Perceptions

Anna Nöteberg, Ellen Christiaanse, Philip Wallage

November 2000

Category: scientificStatus: forthcoming in e-Service Journal, Fall 2001

Universiteit van Amsterdam

Department of Information ManagementRoetersstraat 111018 WB Amsterdam

Http:// primavera.fee.uva.nl/

Copyright © 2000 by the Universiteit van AmsterdamAll rights reserved. No part of this article may be reproduced or utilized in any form or by any means, electronic of mechanical, includingphotocopying, recording, or by any information storage and retrieval system, without permission in writing from the authors.



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Consumer Trust in Electronic Commerce:

The Impact of Electronic Commerce Assurance on

Consumers' Purchasing Likelihood and EC Risk Perceptions

Anna Nöteberg, Ellen Christiaanse, Philip Wallage

Faculty of Economics and Econometrics

Department of Accountancy and Information Management

University of Amsterdam

ABSTRACT: The objective of this study is to assess the impact of third party-provided electronic

commerce assurance on consumers' likelihood to purchase products and services online and their

concerns about privacy and transaction integrity. Study hypotheses are based on marketing theory

concerning the role of trust (e.g. Morgan and Hunt, 1994) and risk (e.g. Murray, 1991 and Roselius,

1971) in consumer decision making. The impact of various product and vendor risk levels on

consumer responses is also tested.

1,109 subjects participated in a 2 (high and low product risk) by 2 (high and low vendor risk) by 3

(third party assurance, self-proclaimed assurance, no assurance) computerized online experiment. As

hypothesized, third-party assurance significantly increased purchasing likelihood and reduced

consumers' concerns about privacy and transaction integrity. However, interestingly, no significant

differences could be detected between different third party assurance providers. Product risk and

vendor risk had as expected negative effects on purchasing likelihood, and vendor risk had a positive

impact on concerns. Also, a significant but weak interaction term was found in the sense that third

party assurance had slightly more impact on purchasing likelihood when the vendor risk condition was

high.

Theoretical predictions are supported. Research findings offer some theoretical insight into the

decision making of online consumers and suggest management implications for online vendors and

third party EC assurance providers such as accountants or consumer unions.

KEY WORDS AND PHRASES: (consumer)trust, assurance seals, electronic commerce,

experimental design



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Index

1. Introduction......................................................................................................................... 4

2. Theory and Hypotheses......... ......... ......... ........ ......... ........ ......... ........ ......... ........ ......... ......... 5

2.1 EC assurance...................................................................................................................6

2.2 Product and vendor specific risks ........ ......... ........ ......... ........ ......... ........ ......... ........ .........6

2.3 Consumer responses........................................................................................................7

2.3.1 Likelihood to purchase (DV 1)......................................................................................7

2.3.2 Concerns (DV 2 and 3).......... ......... ........ ......... ........ ......... ........ ......... ........ ......... ......... .7

2.4 Hypotheses .....................................................................................................................7

2.4.1 Product types (IV 1).....................................................................................................7

2.4.2 Vendor types (IV 2) ........ ......... ........ ......... ......... ........ ......... ........ ......... ........ ......... .......8

2.4.3 EC assurance (IV 3).....................................................................................................8

3. Method................................................................................................................................. 9

3.1 Procedures....................................................................................................................10

3.2 Dependent measures......................................................................................................11

4. Results ............................................................................................................................... 12

4.1 Sample demographics....................................................................................................12

4.2 Manipulation check testing ........ ......... ......... ........ ......... ........ ......... ........ ......... ........ ....... 13

4.2.1 Products....................................................................................................................13

4.2.2 Vendor types .............................................................................................................13

4.2.3 EC assurance .............................................................................................................14

4.3 Dependent measures......................................................................................................14

4.4 Preliminary analyses......................................................................................................15

4.5 Hypothesis testing.........................................................................................................16

4.6 Post-hoc observations ........ ........ ......... ......... ........ ......... ........ ......... ........ ......... ........ ....... 19

5. Conclusion......................................................................................................................... 20

References................................................................................................................................. 22



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1. Introduction

The purpose of this study is to examine the impact of electronic commerce third party assurance on

consumers, as reflected in consumers' risk perceptions and intentional purchasing behavior. Since this

is one of the first studies to investigate the value of EC assurance on consumer behavior, it can be

labeled exploratory in nature.

The reasons behind customer buying behavior in online environments and electronic channels are

important issues for researchers and practitioners interested in the consumer effects of electronic

commerce developments. It is of vital importance to carefully design electronic commerce strategies

and position products and services in such a way in these online environments that the likelihood of

purchasing is maximized. The careful design of such customer interaction and channel configuration is

crucial to most firms (Mohr Fischer and Nevin 1996). The successful commercial development of the

Web depends on a variety of interdependent structural factors and despite the fact that the Web offers a

number of important benefits to both consumers and firms (Hoffmann Novak and Peralta 1999), most

online firms are still searching for the best strategies and business models for online commerce.

A recurring issue in all electronic commerce research and one of the main impediments to growth of

electronic commerce is the role of customer concerns and perceived risks in relation to the likelihood

of purchase (Houston and Taylor 1999, Jarvenpaa Tractinsky and Vitale 2000, Farrell Leung and

Farrell 2000). It is widely assumed that the lack of confidence Internet consumers have in this newly

developed marketplace represents a major impediment against full-scale integration of the Internet

marketplace with modern business. One of the most crucial issue that Internet consumers have

identified is fear and distrust regarding loss of personal privacy associated with the emerging

electronic marketplace (Wang Lee and Wang 1998; Novak Hoffman and Peralta 1999; AICPA 1998).

The factors that influence and reduce consumers’ willingness to engage in online exchange

relationships are mainly related to privacy (Hoffmann Novak and Peralta 1999), transaction integrity

(Farrell Leung and Farrell 2000) and trust (Doney and Cannon 1997, Wang et al. 1998, Jarvenpaa et al.

1999, Cheskin 1999). EC third party assurance is expected to relieve online customers from their

privacy concerns and to increase their purchasing willingness.

The importance of this study is found in both its theoretical contributions and implications for practice.

It offers some interesting insights into the decision making process of online consumers with respect to

perceived risks and trusting behavior.

Regarding the management implications of this study, online vendors, third party assurance services

and consumer protection alliances can utilize study findings to develop targeted marketing programs

that consider consumer preferences in EC assurance.

The next section of the paper develops the underlying theory and proposes study hypotheses.

Following, research procedures are discussed and the results of the experiment are presented.



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Summarized study findings, suggestions to online vendors and assurance providers and future research

ideas are given in the final section of the paper.

2. Theory and Hypotheses

The underlying theory for this paper is primarily taken from marketing theory on trust and risk issues.

Marketing theory and practice have witnessed a shift from discrete to relational marketing during the

past two decades (Morgan and Hunt 1994). In line with these developments, buyer trust toward a seller

is increasingly regarded as a key mediating construct in any buyer-seller relationship. Morgan and

Hunt's commitment-trust theory for example suggests that trust – together with commitment – leads

directly to cooperative behaviors that are conducive to marketing success (Morgan and Hunt 1994). Anumber of studies have already proven that increased trust toward a seller has a positive impact on

future interaction, purchasing and commitment intentions (e.g. Morgan and Hunt 1994, Garbarino and

Johnson 1999, Crosby Evans and Cowles, 1990). Accordingly, EC firms who build greater trust with

their customers should have a higher likelihood of increased customer acquisition and sales.

When it comes to antecedent factors that influence the trust building process, several items have been

found significant. Doney and Cannon (1997) for example suggest that trust requires a degree of

objective credibility. Moorman, Deshpandé and Zaltman (1993) note that uncertainty reduction is a

critical element of trust. This view is strengthened by marketing theorists that study the role of risk inconsumer decision making. For example, Roselius (1971) suggests several risk reduction methods that

consumers use in their purchasing behavior. One of these is labeled endorsements which refers to the

dependence on endorsements or testimonials from a person like the customer, from a celebrity, or from

an expert on the product.

Especially in the area of electronic commerce, the need for consumer trust has been emphasized

heavily. Since consumer concerns and perceived risks are particularly high in online purchasing, the

need for trust is also considerably high if the desired outcome is to increase purchases over the Web.

Many authors have already stressed this need (e.g. Hoffman et al. 1999, Jarvenpaa et al. 2000),

however, empirical studies in the field are rare (for a few exceptions see Jarvenpaa et al 2000, Farrell

Leung and Farrell 2000). Perceived risks or concerns that previous studies have found to be

considerably acute among online consumers are in the area of privacy loss and transaction integrity

(e.g. Yankelovich Partners 1997, Wang Lee and Wang 1998, Novak Hoffman and Peralta 1999, Farrell

Leung and Farrell 2000).



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2.1 EC assurance

Taking these theoretical and practical implications into account, the presence of EC third party

assurance can be considered a means by which consumer trust in an Internet store is increased through

a reduced sense of uncertainty and risk. Assurance is provided on the most important consumer

concerns, which in turn might reduce perceived risks and lead to a higher degree of trust, similar to

Roselius' (1971) risk reduction method 'endorsements': An independent third party, such as an

accountant, a bank or a consumer union steps in and provides assurance on various business standards,

such as the handling of customer privacy and transaction integrity. EC assurance may also provide an

increased sense of objective credibility, which again has a positive impact on consumer trust and

trusting behavior. This paper is primarily sought to measure the value of third party and self-provided

EC assurance.

2.2 Product and vendor specific risks

In addition to EC assurance, we chose to measure the impact that risk associated with different product

types and vendor types would have on the subjects' trust and their risk perceptions. In the risk

literature, product-specific risk is suggested to have a significant influence on consumer attitudes and

behavior. Risk associated with the type of product has six components: financial, performance, social,

psychological, and time/convenience loss (Murray 1991).

Two risk factors seemed most pertinent to this study: financial and performance risk. Financial risk is

essentially the likelihood that the transaction outcome will have less utility to the acquiring party than

the exchanged economic resources. Financial risk thus increases proportionally with the value of the

exchanged resources. Performance risk focuses on perceived quality of the exchange outcome, such

that highly variable outcomes yield high levels of perceived performance risk. Taken together, higher

levels of financial and performance risk create greater uncertainty for consumers making a purchasing

choice.

Regarding the risk represented by the type of vendor, the literature on trust in marketing suggests that

a vendor's reputation and the buyer's familiarity with the vendor have an impact on the buyer's trust

and perceived risks (e.g. Dasgupta 1988, Ganesan 1994, Anderson and Weitz 1989, 1992). Anderson

and Weitz (1992) for example found that a retailer's trust in their vendor was higher when they

perceived the vendor to possess a reputation for fairness. A negative reputation on the other is likely to

reduce trust between channel members (Anderson and Weitz 1992). Further, the study conducted by

Jarvenpaa et al. (2000) confirms the impact of vendor reputation on the creation of trust and

purchasing willingness in an EC setting. In terms of risk, we argue that an unknown vendor (low

reputation) represents high risk, while a well-known vendor (high reputation) can be coded as low risk

in the consumer's mind.



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2.3 Consumer responses

2.3.1 Likelihood to purchase (DV 1)

Although numerous definitions of the term trust have been offered over the years, it is generally

described as an expectancy, a belief or a feeling that the word of an exchange partner can be relied on(Rotter 1967, Crosby Evans and Cowles 1990, Moorman Deshpande and Zaltman 1993, Morgan and

Hunt 1994). Following Pearce (1974), Cowles (1997) suggests a distinction between trusting behavior

and cognitive trust. In this sense, the actual purchase of a product or a service would be a so-called

trusting behavior, while in the second form, trust would have to be measured directly. In this study, we

consider the customer likelihood to purchase from an online store a trusting behavior, and use it as an

indirect measure of trust.

The theory of reasoned action (TRA) and the theory of planned behavior (TPB) presume that volitional

behavior is determined by intentions to act (see, for example, Ajzen and Fishbein 1980; Bagozzi 1981;

Ajzen 1985). A major determinant of intentions, in turn, is the actor’s attitudes towards the behavior,

in this case partly determined by the consumer's perceived risks. TRA and TPB have been evaluated

and supported in many contexts (Ajzen 1985), including IT usage behaviour (Taylor and Todd 1995).

Internet shopping behavior shares the volitional nature of the phenomena which TRA tries to explain

and predict. Thus, for the purpose of studying the impact of EC assurance on consumer behaviour, we

assume that the degree to which people express their likelihood (or intention) to buy from a certain site

relative to other sites is a reasonable predictor of actual purchase behavior from this site relative to the

others.

2.3.2 Concerns (DV 2 and 3)

As mentioned previously, the two major concerns that stand in the way of trustworthiness in electronic

commerce trading are the loss of personal privacy (Wang Lee and Wang 1998; Novak Hoffman and

Peralta 1999; AICPA 1998) and transaction integrity (Farrell Leung Farrell 2000). These concerns are

expected to be reduced by the presence of EC assurance. We chose to ask subjects about the likelihood

that any of the two concerns would prevent from purchase, which we deemed a sufficient measure of

the perceived risk in these two areas.

2.4 Hypotheses

2.4.1 Product types (IV 1)

The product types that were chosen for the experiment were the purchase of a book, the purchase of a

video camera, the purchase of an intercontinental travel tour, and the purchase of securities. We

expected these products to represent a range of financial and performance risk levels. The purchase of

a book for example is a fairly cheap and in-complex exchange; it is not very expensive (low financial

risk) and the quality of the outcome can only be dissatisfying to a very limited extent. A video camera



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has higher financial risk, and an intercontinental travel tour is more risky in both financial and

performance terms, as the complexity of the service increases the likelihood that something will go

wrong. Finally, the purchase of securities was deemed as the most risky undertaking, as – following

the manipulation description – the customer's whole portfolio is at stake and the outcome is very

unpredictable. Our assumptions were tested among students in a pre-test, resulting in corresponding

risk perception levels for each of the products. Thus follows null-hypothesis 1:

H1: There will be no difference among the following types of products (book, video

camera, travel tour, securitie s) on any of the following consumer responses (a) likelihood

of purchase, (b) concern about transaction integrity and (c) concern about privacy.

Despite using a relatively large range of products, we primarily expected to find differences in the

effects of product risk on consumer responses at the extremes. As the risk difference between books

(low risk) and securities (high risk) was deemed the largest, significant differences in consumer

responses were expected there.

2.4.2 Vendor types (IV 2)

Following studies conducted on the effect of vendor reputation on trust and risk perception, we first

decided to make a distinction between well-known and unknown vendors. However, considering

recent developments where businesses have managed to build reputations with their mere online

presence (e.g. 'Amazon.com'), we were also interested whether consumers' trust would be as high in

such a 'virtual' reputation. Yet, significant differences in consumer responses were expected only at the

extremes, i.e. unknown (high risk) versus well-known (low risk) vendors. The vendor types chosen forthis study were “well-known for electronic sales of the product,“ “well-known only for non-electronic

sales but not for electronic commerce,“ and “completely unknown“. Thus follows null-hypothesis 2:

H2: There will be no difference among the following types of vendors (unknown, well-

known on Internet, well-known in non-electronic market) on any of the following

consumer responses (a) likelihood of purchase, (b) concern about transaction integrity and

(c) concern about privacy.

2.4.3 EC assurance (IV 3)

EC third party assurance was in this study operationalized by using various invented seals of approval.

During the past years, various parties have reacted to increasing consumer concerns by introducing

seals that are placed commercial Web sites, assuring to the consumer that the site is following certain

standards. Who issues this seal and what the criteria consist of differs from one assurance service to

the other. However, many seal providers (e.g. AICPA WebTrust1) base their criteria on consumer

surveys in order to address the right consumer fears and concerns. Providers may consist of e.g. banks,

accountants, consumer unions, and computer companies. The basic idea however is the same with

1 Web assurance logo issued by certified public accountants.



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every assurance seal: reducing consumer concerns and increasing their trust in an electronic purchase

by a third party assurance. Essentially, if a seller fulfils a set of criteria specified by an assurance

provider, it can place the provider’s seal on its Web site. The seal offers assurance to concerned buyers

that the seller meets the standards established by a seal provider. Typically, the logo itself is tamper-

resistant and is linked to the assurance provider’s site, where the user can go to find out more detailed

information about the meaning and the scope of the logo service (Gray and Debrecency 1998).

Instead of only testing the difference between 'seal' and 'no seal', we decided to also study the effect of

various EC assurance providers. As recent developments in the electronic commerce market show, any

party is potentially able to start such an assurance service, whether independence and a positive

reputation is granted (e.g. CPAs) or not. We were interested to find out whether consumers would

react diversely in their purchasing likelihood and stated concerns to such provider differences.

Additionally, many online stores decide to post a self-reporting statement on their Web site where they

claim to comply with 'established' electronic commerce standards. We decided to include such a self-

report as one of the assurance types as well. The six assurance-provider types chosen in this study

were thus: (1) independent accountants, (2) banks, (3) computer industry, (4) consumer unions, (5)

self-reporting statement of compliance with “established“ electronic commerce standards, and (6) no

assurance.

H3: There will be no difference among the following types of EC assurance (Accountant’s

assurance, Bank’s assurance, Consumer Union’s assurance, Computer Industry’s

assurance, self-proclaimed assurance, no assurance) on any of the following consumer

responses (a) likelihood of purchase, (b) concern about transaction integrity and (c)

concern about privacy.

Regarding the issues that each seal would assure on, they were derived from the standards used by

similar existing seals, such as 'AICPA WebTrust' for the accountant's assurance, 'BBB-Online' for the

consumer union's assurance, 'TRUSTe' for the computer industry's assurance. The self-proclaimed

statement assured on the same principles as the accountant's seal.

Again, we expected to find differences in consumer responses at the extremes, primarily between the

presence (low risk) and the absence (high risk) of a seal, and possibly the self-proclaimed assurance as

standing in the middle (medium risk).

3. Method

To test the hypotheses, a 4 by 3 by 6 experiment was developed to examine the impact of EC

assurance, product risk and vendor risk on consumers' likelihood to purchase and their risk perceptions

related to privacy and transaction integrity. In this section, the procedures and dependent measures are

described.



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3.1 Procedures

Subjects were attracted to an online experiment through banners situated at several Web sites, such as

universities, research institutes and companies. Participating in the experiment offered subjects a

chance on winning a new multimedia PC. The procedures consisted of presenting to each subject three

randomly preassigned simulated purchase scenarios. Each scenario described the purchase of a specific

product (product risk, IV1) from a specific vendor (vendor risk, IV2) and assured by a specific EC

assurance provider (IV3). Subjects were encouraged to read the description of their scenario, i.e. the

independent variables, carefully.

The product risk was reflected in the product to be purchased. Ranging from low to high risk products,

the following were used: book, video camera, travel tour or securities. The manipulation read as

follow:

1. Book: You are considering entering into an electronic -commerce transaction involving

the purchase of a bestseller novel by John Grisham through the Internet.2. Video camera: You are considering entering into an electronic-commerce transactioninvolving a video camera.3. Intercontinental travel tour: Purchase of a comprehensive intercontinental travel tourpackage that includes air fares, hotels, meals, bus excursions, admission to various tourattractions, and several local transportation transfers.4. Securities: You are considering entering into an electronic-commerce transactioninvolving financial services through the Internet. You currently have a substantialportfolio of monetary investments, including your total retirement (pension) funds andother sizable investment in securities. Indeed, a high portion of your wealth and “nest egg“for the future are included in your investment portfolio. You will establish a personalidentification number (called a “PIN“ or a password). In order to verify your investmentaccounts or make a purchase, sale, trade, or withdrawal, you may do so electronically viathe Internet from anywhere in the world by simply providing your password (or “PIN“)and indicating exactly what you want to do.

The vendor risk factor was manipulated as either “well-known for electronic sales of the product,“

“well-known only for non-electronic sales but not for electronic commerce,“ or “completely

unknown“. The manipulations read as follow:

1. Well-known 'online': The site you are going to visit is one of the largest online'product' stores in the world. It has been online for 5 years now and has millions of customers world wide.2. Well-known 'off-line': The site you are going to visit is a very well-known company inthe traditional, non-electronic world. The company just opened its first electronic outlet.3. Unknown: The site you are going to visit does not exist in the real world. It has beenonline for a while now, a clear track record cannot be traced.

Finally, the EC assurance factor was manipulated as either independent accountants, bank, computer

industry, consumer union, self-reporting statement of compliance with “established“ electronic

commerce standards, or no assurance. The manipulations read as follow:



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1.-4. Accountant's assurance, bank's assurance, computer industry's assurance, andconsumer union's assurance: You will be asked to look at an online shop site. You will seea “seal of assurance“ at the top. You are now asked to read the information about this sealbelow. The same information can be accessed by clicking the seal on the online shop site, sothat you can review it at any point.

'N ame of the seal'

To obtain the 'Name of the seal', online businesses must meet the following standards.

Following, the subject was to read the respective standards.

5. Self-reporting statement: You will be asked to look at an online shop site. You will see a“seal of assurance“ at the top. You are now asked to read the information about this sealbelow. The same information can be accessed by clicking the seal on the online shop site, so

that you can review it at any point.

'S elf Report'

The key element of our company's electronic commerce activities is that we base them on thefollowing three principles of electronic commerce:

Business Practices Disclosure Principle (click here)

Transaction Integrity Principle (click here)

Information Protection Principle (click here)

Clicking on the three principles would enable the subject to read the descriptions.

6. No assurance: You will be asked to look at an online shop site. It doesn't have a seal of assurance.

3.2 Dependent measures

After reading the descriptions, subjects were asked to provide feedback on the dependent variables,

which measured subjects' likelihood to purchase and their privacy and transaction integrity concerns.

The questions read as follow:

1. How likely are you to purchase this item electronically?

2. How likely are the following concerns to prevent you from purchasing this itemelectronically from this vendor?

2.a) Concern about integrity of the transaction would prevent me from purchasing thisproduct by electronic commerce from this vendor.

2.b) Concern about privacy would prevent me from purchasing this product by electronic

commerce from this vendor.

All three dependent variables were measured using a seven-point Likert-type scale, ranging from 1

(corresponding ‘extremely likely’) to 7 (corresponding ‘extremely unlikely’).

Having completed one scenario, each subject went through the same process another two times, again

with a randomized combination of product, vendor and assurance types.

In the final part of the experiment, subjects were asked to provide feedback on several demographic

questions, such as age, gender, Internet and electronic commerce experience.



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4. Results

4.1 Sample demographics

Overall, the study included 1,109 participants. The sample sizes within each of the in total (4x3x6) 72

treatment conditions were between 11 and 37. The demographic profile of the sample is summarized

in table 1 and the sample’s computer and online shopping experience is summarized in table 2. The

profile reflects the relatively high Internet and computer experience of the participants. All participants

are regularly online, although relatively few had engaged in commercial transaction on the Internet.

Furthermore, subjects are relatively young and almost 80% of the sample consists of male subjects, in

line with previous usage studies conducted (Li Kuo and Russel 1999, Lohse and Spiller 1998).

No significant demographic differences were found across treatment conditions.

Table 1: Demographic profile of participants Variable Value %Age 18-24

25-2930-3435-3940-4950-6465 or olderMissing

Total

26.225.914.811.115.95.30.30.6

100.0Sex FemaleMaleMissingTotal

21.178.95.8100.0

Table 2: Internet and computer demographics

Variable Value %Typically online Everyday

2-3 days a week4-6 days a weekOnce a week2-3 days per monthOnce a monthLess than once a month

7111.813.62.90.20.30.2

Ever purchased anything online Never

Once2-4 times5-10 timesMore than 10 times

43.2

16.419.810.510.0

Rating of computer expertise Extremely highVery highQuite highMediumQuite lowVery lowExtremely low

15.625.233.620.23.81.10.5

Computer and Internet available athome

YesNo, but expecting one within the next24 monthsNo

81.87.6

10.4Computer and Internet available at

work

Yes

No, but expecting one within the next24 monthsNo

80.5

3.2

16.2



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4.2 Manipulation check testing

4.2.1 Products

Due to the computerized nature of the experiment the researchers are confident that subjects in each

product condition read the products description previously shown, and only that description.Additionally, for the product risk manipulation, subjects responded to the following three questions

after completion of the experimental tasks:

What is according to you the risk of the following products when making a purchase on theInternet? Please give a rating for each of the products.

• Bestseller novel

• Video camera

• Intercontinental travel tour

• Securities

Subjects rated the risk for each product type on a scale ranging from 1 (corresponding 'extremely low')

and 7 (corresponding 'extremely high'). The four respective means were in the anticipated direction, as

the book means was 2.72 (1.41), the video camera means was 4.53 (1.35), the travel tour means was

4.79 (1.43), and the securities means was 5.03 (1.84). The t-test and the post-hoc tests on these ratings

shows that all differences except the one between video camera and travel tour were significant.

Hence, the product risk manipulation was deemed partially successful.

4.2.2 Vendor types

Again, due to the computerized nature of the experiment we are confident that subjects in each vendor

condition read the vendor description previously shown, and only that description. Additionally, for

the vendor risk manipulation, subjects responded to the following three questions after completion of

the experimental tasks:

What is according to you the risk of the following vendor types when making a purchase on

the Internet? Please give a rating for each of the vendor types.

• Vendor very well known only in the traditional, non-electronic world.

• Vendor very well known only on the Internet.• Vendor not know neither on the Internet nor in the traditional, non-electronic world.

Subjects rated the risk for each vendor type on a scale ranging from 1 (corresponding 'extremely low')

and 7 (corresponding 'extremely high'). Vendors well-known for their non-electronic sales were rated

at a mean of 3.52 (standard deviation 1.58), vendors well-known for their electronic sales received the

mean rating of 3.52 (s.d. 1.51), and the unknown vendor risk mean was 5.3 (s.d. 1.85). The t-test and

post-hoc tests revealed that the difference between the risk rating of unknown vendors on one side and

well-known vendors (either electronic or non-electronic) on the other was significant.



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Given all the evidence we deem that the vendor manipulation was partially successful. That is, subjects

perceived a risk difference between not familiar and the two familiar vendors, but they did not

perceive a difference between well-known vendors.

4.2.3 EC assurance

For the assurance manipulation check testing, subjects were asked how well they understood the

description of the assurance seal. The responses were coded as 1=Not at all, 2=Very little, 3=Rather

poorly, 4=Moderately well, 5=Quite well, 6=Reasonably well, 7=Very well. The mean response to this

question was 5.92 (s.d. .98), which we deem as relatively high.

An ANOVA was conducted, using assurance type as the independent variable and the question of how

well subjects understood the description of the assurance seal as the dependent variable. Differences

were in three cases significant (accountant-bank, accountant-consumer union, and consumer union-self

report). However, even when significant, the mean differences were by the authors deemed asrelatively small and trivial (in all cases less than .5), and thus not meaningful in a practical sense. This

results in a successful assurance manipulation.

4.3 Dependent measures

Table 3 shows mean correlations of the three dependent measures likelihood of purchase (mean=4.65,

s.d.=1.89), likelihood that concern about transaction integrity would prevent purchase (mean=4.6,

s.d.=1.88) and likelihood that concern about privacy would prevent purchase (mean=4.53, s.d.=1.87).

A high correlation can be reported. The correlation between likelihood of purchase and the two

concern likelihoods is clearly negative, while the correlation between the two concern likelihoods is

positive. The negative correlation is not very surprising, since subjects who felt that their concerns

would refrain them from purchasing, expressed this in their likelihood of purchase as well. The stated

positive correlation might be explained by the fact that subjects tended to be concerned about both

privacy and transaction integrity or not at all.



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Table 3: Correlations of dependent variables

Correlations

1,000 -,295** -,269**

, ,000 ,0001610 1601 1598

-,295** 1,000 ,713**

,000 , ,000

1601 1608 1600

-,269** ,713** 1,000

,000 ,000 ,

1598 1600 1605

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Likelihood of purchasing

this item electronically

Likelihood that concernabout integrity of thetransaction would prevent

Likelihood that concernabout privacy would

prevent from purchasing

Likelihood of

purchasingthis item

electronically

Likelihoodthat

concern

aboutintegrity of

thetransaction

wouldprevent

frompurchasing

this

productfrom thisvendor

Likelihood

thatconcern

aboutprivacywouldprevent

frompurchasing

this

productfrom thisvendor

Correlation is significant at the 0.01 level (2-tailed).**.

4.4 Preliminary analyses

A MANCOVA model was conducted using product risk, vendor risk and assurance type as the

independent variables and gender, availability of a computer and Internet at home, self-rating of

computer expertise as covariates2. The product factor was significant for DV 1 and DV 2. The vendor

and the assurance factors were significant for all three DVs. Finally, the interaction term between

vendor and assurance was significant.

The post-hoc tests confirmed to a large extent our expectations about extremes within each

independent variable regarding the effect that each factor had on our dependent variables: First, the

significant effect that was found for the product risk factor on DVs 1 and 2, was explained only by a

mean difference between book and all other product. In other words, likelihood of purchase was higher

and likelihood that concern about transaction integrity would prevent purchase was lower regarding

the purchase of a book compared to all other products.

Second, regarding the vendor risk factor, there were only significant differences between 'well-known

for it electronic sales' and 'well-known for its non-electronic sales' on one side and 'unknown' on the

other. Thus, subjects were more likely to purchase and less likely to be influenced by their concerns

2

The following covariates were not considered in subsequent analyses due to their insignificance: Age, Frequencyonline use, Ever purchased anything online and Availability of computer and Internet at work.



16

when buying from a well-known vendor, whether well-known online or offline, than from an unknown

vendor.

Third, significant differences within the assurance factor were mainly found between any of the four

third party assurance levels, self-report and no assurance. Neither subjects' likelihood to purchase nor

their likelihood to refrain from purchasing due to privacy and transaction integrity concerns were

significantly different across 'accountant', 'bank', 'computer association' and 'consumer union'

assurance types.

These considerations encouraged us to collapse some of the levels within all independent variables

before conducting the final hypothesis testing. Table 4 shows the result of the collapsed levels within

all our independent variables.

Table 4: Collapsed levels within all IVs

IV Before AfterProduct • Book

• Video camera• Travel tour• Securities

• Book

• Other products

Vendor • Well-known for electronic sales of theproduct

• Well-known only for non-electronic salesbut not for electronic commerce

• Completely unknown

• well-known• unknown

Assurance • Independent accountant• Bank• Computer industry• Consumer unions• Self-reporting statement of compliance

with “established“ electronic commercestandards

• No assurance

• third party assurance• self-reporting statement of compliance

with “established“ electronic commercestandards

• no assurance

Regarding the covariates included in the MANCOVA model, significant differences were found for

gender, availability of a computer and Internet at home, and self-rating of computer expertise. Men

would generally be more likely to make online purchases than women; however no main effect was

found for gender on the other DVs. Those subjects who have a computer and Internet available at

home would overall be more likely to purchase and would be less likely to let concerns about privacy

prevent them from purchase than those who don't have a computer and Internet available at home.Similarly, subjects with a computer and Internet at work were more likely to purchase than those don't.

Finally, subjects who rated their computer expertise as medium were significantly more likely to

refrain from purchasing because of privacy and transaction integrity concerns than highly computer

literate subjects.

4.5 Hypothesis testing

As already mentioned, a significant interaction term was found between the vendor and the assurance

factor. The graph in Figure 1 illustrates the nature of this interaction. Holding vendor risk high (i.e.,

unknown vendor), the likelihood of purchase increased from 2.62 (no assurance), via 3.41 (self-



17

proclaimed assurance), to 4.20 (third party assurance). Holding vendor risk low (i.e., known vendor),

the significant increase from self-proclaimed (5.01) to third party assurance (5.22) disappears.

Another conclusion to be drawn from the interaction graph (Figure 1) is that no significant difference

in purchasing likelihood can be found between the combination “known/no assurance“ and the

combination “unknown/third party assurance“.

However, due to the weak nature of the present interaction, the authors deem that main effects can still

be taken into account. Using the collapsed independent variables (see Table 4), separate ANCOVA

models were run for all dependent measures. ANCOVA results are shown on Table 5.

Figure 1: Graphical illustration of interaction between vendor risk and assurance type

Likelihood of purchasing this item electronically

0

1

2

3

4

5

6

T h i r d p a r t y

a s s u r a n c e

S e l f -

p r o c l a i m e d

a s s u r a n c e

N o

a s s u r a n c e

Knownvendor

Unknownvendor

Table 5: ANCOVA test results

Panel A: Likelihood of purchasing this item

Source d.f. Mean Square F-Ratio p-value HypothesisProduct 1 97.189 35.753 .000 H1 rejectedVendor 1 202.932 74.652 .000 H2 rejectedAssurance 2 72.395 26.632 .000 H3 rejectedInteractionassurance *vendor

2 10.085 3.71 .025

Panel B: Likelihood that concern about transaction integrity would prevent from purchase

Source d.f. Mean Square F-Ratio p-value HypothesisProduct 1 10.323 3.098 .079 H1 supportedVendor 1 42.542 12.769 .000 H2 rejectedAssurance 2 23.453 7.039 .001 H3 rejected

Panel C: Likelihood that concern about privacy would prevent from purchaseSource d.f. Mean Square F-Ratio p-value HypothesisProduct 1 8.263 .002 .960 H1 supported

Vendor 1 39.527 11.890 .001 H2 rejectedAssurance 2 27.666 8.322 .000 H3 rejected



18

Hypothesis 1 posited that there would be no difference across products (books and other products) on

either of the three dependent measures (likelihood of purchase, likelihood that concern about privacy

would prevent purchase, and likelihood that concern about transaction integrity would prevent

purchase). No interaction effects with other dependent variables were found. Table 5, panel A

illustrates a significant ANCOVA (F=35.753, p=.000) on likelihood of purchase, indicating one or

more significant differences among the conditions. Pare wise comparisons show that the likelihood of

purchasing a book (mean=5.3, st.d.=1.75) is significantly higher than for the other products

(mean=4.44, st.d.=1.89). Table 5, panel B and C illustrate insignificant ANCOVAs on the likelihood

that concern about privacy and concern about transaction integrity would prevent purchase.

Concluding, hypothesis 1 is rejected, as product risk has a significant effect on one of the three

dependent variables.

Hypothesis 2 posited that there would be no difference across vendors (well-known and unknown) on

either of the three dependent measures (likelihood of purchase, likelihood that concern about privacy

would prevent purchase, and likelihood that concern about transaction integrity would prevent

purchase). Disregarding the interaction effect with assurance type for a moment, we can conclude from

the ANOVA illustrated in Table 5, panel A that vendor risk has a significant effect on likelihood of

purchase (F=74.652, p=.000). Pare wise comparisons show that the likelihood of purchasing from a

well-known vendor (mean=5.05, st.d.=1.70) is higher than from an unknown one (mean=3.77,

st.d.=2.02).

Further, significant ANCOVAs are shown for the remaining two DVs (likelihood that concern about

transaction integrity and likelihood that concern about concern about privacy would prevent purchase)

in Table 5, Panels B and C. In both cases, likelihood that concerns would prevent from purchase are

higher for unknown vendors. The mean likelihood that concern about transaction integrity would

prevent purchase is 5.02 (st.d.=1.82) for an unknown vendor and 4.41 (st.d.=1.87) for a well-known

one. The mean likelihood that concern about privacy would prevent purchase is 4.84 (st.d.=1.83) for

an unknown vendor and 4.39 (st.d.=1.87) for a well-known one. Concluding, hypothesis 2 is rejected

as there are significant differences between vendor types in relation to the three dependent variables.

Hypothesis 3 posited that there would be no difference across EC assurance types (third party

assurance, self-report, and no assurance) on either of the three dependent measures (likelihood of

purchase, likelihood that concern about transaction integrity would prevent purchase, and likelihood

that concern about privacy would prevent purchase). Again, disregarding the reported interaction

effect with vendor type, Table 5, Panel A illustrates significant mean differences across assurance

types for likelihood of purchase. Overall, the mean likelihood of purchase with third party assurance is

4.96 (st.d.=1.77), with a self-report 4.53 (st.d.=1.92) and without any assurance 3.45 (st.d.=1.93). All

differences are significant.

Further, significant mean differences are found for the remaining two dependent variables, however

not for the combination self-report and no assurance. The likelihood that concern about transaction



19

integrity would prevent purchase is higher with a self-report (mean=4.77, st.d.=1.83) and without

assurance (mean=5.02, st.d.=1.82) than with third party assurance (mean=4.45, st.d.=1.89). Similarly,

the likelihood that concern about privacy would prevent purchase is higher with a self-report

(mean=4.72, st.d.=1.81) and without assurance (mean=4.95, st.d.=1.83) than with third party assurance

(mean=4.38, st.d.=1.88). Concluding, null-hypothesis 3 is rejected.

4.6 Post-hoc observations

In addition to the main consumer responses, subjects were in each condition asked to state the

importance of the seal in their purchasing decision (using a scale ranging from 1 corresponding

'extremely unimportant', and 7 corresponding 'extremely important'). The exact wording of the

question was "How important would this seal be when making a purchasing decision?". To test

whether the type of assurance had a significant impact on subjects' responses to this question, we

reduced the adjusted IV assurance type to two levels, i.e. third party assurance and self-proclaimedassurance. Naturally, the 'no seal' condition needed to be eliminated for this test. A MANCOVA model

was conducted using product risk, vendor risk and assurance type as the independent variables. An

interaction term was found between vendor and assurance type, quite similar to the one found on

likelihood of purchase. The interaction is illustrated in Figure 2. Essentially, the importance of

assurance is higher for a well-known vendor, but the importance increase from self-proclaimed to third

party assurance is steeper for purchase from an unknown vendor.

Figure 2: Graphical illustration of interaction between vendor risk and assurance type

Importance of assurance when making purchasing decision

0

1

2

3

4

5

6

3 r d p a r t y

S e l f

Knownvendor

Unknownvendor



20

5. Conclusion

This paper studies the impact of EC third-party assurance on consumers' purchasing likelihood and

their EC concerns regarding privacy and transaction integrity. Furthermore, the effect of product and

vendor risk is tested. Regarding all independent variables (assurance, product risk, and vendor risk),

impact differences were found at the extremes, which is why a reduced set of IV levels was used for

subsequent hypothesis testing. Product risk was reduced to 'books' (low risk) and 'other products' (high

risk), vendor risk was reduced to 'well-known' (low risk) and 'unknown' (high risk), and assurance type

was reduced to 'third party assurance', 'self-proclaimed assurance', and 'no assurance'. It is interesting

to note that no significant differences on any of the consumer responses could be discovered across 4

different third-party assurance services. This finding strongly indicates that EC third party assurance

can potentially be offered by a whole range of institutions, without the necessity of absolute

independence.

Likelihood of purchase was as expected highest for low product risk, low vendor risk, and third party

EC assurance. However, there was a significant interaction term between vendor risk and assurance

type, indicating that purchase from a well-known vendor is equally likely with a third party assurance

and a self-proclaimed assurance, while third-party assurance had a significant effect when the vendor

was unknown. It can thus be concluded that third-party assurance is unnecessary for vendors with a

high reputation, while unknown vendors can enhance consumers' purchasing likelihood by obtaining

EC third party assurance.

Likelihood that concern about transaction integrity and privacy would prevent purchase was highest

for high vendor risk, self-proclaimed assurance and no assurance. Thus, well-known vendors can be

expected to reduce consumers' concerns by their reputation. The presence of third party EC assurance

also had a risk reducing effect, while both self-proclaimed assurance and no assurance increased

consumers' concerns about privacy and transaction integrity.

Some policy implications may be suggested regarding study results. First, as the results show that thirdparty EC assurance has an impact on consumers' likelihood to make an online purchase, assurance

providers can be encouraged to carry on and develop their services. Second, such providers should

target their assurance services toward relatively new Internet companies without recognized

reputations with their potential customers. Likewise, Internet companies with low familiarity in the

marketplace should consider investing in third party assurance in order to increase their credibility and

reduce consumers' perceived risks and concerns. For well-known Internet vendors on the other hand it

may suffice to put out a policy statement on privacy and transaction integrity issues.

Several study limitations need to be stated at this point. First, as participation in the experiment wasopen for anyone interested, we may be facing the problem of a self-selected sample. However, this



21

problem is partially reduced by checking for demographic effects. Second, it is important to realize

that we measured the perception or attitude of possible consumers toward the likelihood of buying

under certain conditions and not the actual behavior in real buying situations. Research in behavioral

psychology (Ajzen and Fishbein, 1980) addressed the issue that attitudes are relatively poor predictors

of individual behavior. Third, we only considered purchasing risks related to vendor risk and product

risk; there may well be other EC risk factors that have an influence on consumers' perceptions of third

party EC assurance. Fourth, the fact that we found no significant differences across third party

assurance providers may be explained by their case character. No real assurance seals were chosen

which might have induced a higher sense of trust for the participants.

In this study, we primarily looked at one-time discrete transactions over the Internet, and how one risk

reliever, namely EC third party assurance, would affect consumers’ purchasing likelihood and their

concerns about transaction integrity and privacy. We tried to link our study to the increasingly

important topic of trust, but we do realize that the possibilities for further research in this area are

enormous.

First, consumer trust in electronic commerce should in future studies be measured more specifically,

instead of using indirect measures through purchasing willingness and risk perception. Measurement

scales for trust are available (e.g. Doney and Cannon 1997, Johnson-George and Swap 1982, Rotter

1967) and can easily be applied to the business-to-consumer electronic commerce setting as well.

Second, other factors that have an influence on consumer trust in electronic commerce should be taken

into account in future studies. Among these are vendor specific characteristics (size, reputation, site

navigation, etc) and customer specific characteristics (risk/trust propensity, Internet/computer literacy

and experience, etc.) as well as situational elements. Other third parties than assurance seal providers

intended to increase consumer trust exist today, such as site rating services or portals. These represent

another interesting topic for investigation.

Finally, this study’s focus was on mere one-time transactional exchanges between consumers and

Internet vendors. Since trust develops over time it might be interesting to look at long-term relational

interactions between vendors and customers over the Internet and their influence on the trust-building

process. Here, different trust and relationship levels should be taken into account. A longitudinal

research design would be appropriate to study such long-term processes.

Concluding, although more research is needed about the construction of trust in both business-to-

consumer and business-to-business electronic commerce, this study provides some interesting

indications on the effects of third party assurance on consumers’ purchasing intentions under various

conditions. The significant impact of assurance has been shown and the importance of a vendor’s

reputation has been revealed empirically.



22

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