An Empirical Study of the Causal Antecedents of Customer Confidence in E-Tailers
First Monday

An Empirical Study of the Causal Antecedents of Customer Confidence in E-Tailers by Sandeep Krishnamurthy

Why is it that consumers are very confident with an e-tailer such as Amazon.com and lack the same confidence when it comes to a smaller e-tailer such as supremevideo.com? In this paper, we attempt to answer this important question by examining the antecedents to customer confidence in e-tailers, using secondary data. Our findings indicate that the ease of use of a site, the level of online shopping resources, and the presence of a trusted third party seal all positively impact the level of customer confidence. Interestingly, online relationship services did not have an impact on consumer confidence. Larger firms may have a small edge. We also find that there are no large differences in the results across different product categories.

Contents

Introduction
Brief literature review
Conceptual framework
Sample characteristics
Regression analysis
Conclusion and discussion

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Introduction

We have witnessed an explosion in the sale of products from businesses to consumers over the Internet in recent years. In 1997, individual consumers spent $US2.6 billion in such transactions over the Internet. By 1998, the total spent had more than tripled to $US8 billion. In 1999, the total spending in the fourth quarter alone ($US7.5 billion) nearly matched the spending in all of 1998 [1].

This dramatic increase in overall business-to-consumer commerce has been accompanied by an exponential increase in the number of e-tailers. Fueled by the early success of e-tailers such as Amazon.com (Kotha, 1998; Kotha and Rindova, 1999), a number of firms have joined the fray. As a result, the level of consumer choice today is unprecedented. The new e-tailers include "click and mortar firms" (e.g. www.barnesandnoble.com), i.e., retailers with previous experience in brick and mortar stores as well as purely Internet based retailers (e.g. www.etoys.com).

From the consumer's perspective, the perceived risk in e-commerce is greater vis-à-vis purchases that can be made at brick and mortar retail stores. In e-commerce, typically, there is a temporal separation of payment and product delivery [2]. As a result, the consumer must provide personal information (e.g., name, address and phone number) and payment information (e.g. credit card number) before taking delivery of the product. In addition, the shopping process itself may be onerous.

For the most part, the consumer can assess service quality only after the fact. For example, the consumer has no assurance that the product will contain the features that he or she ordered or if the product will be delivered on the promised date. In the case of certain "experience" products (Nelson, 1974), the consumer has no idea if the product meets his or her needs until he or she actually uses the product.

Gaining the consumer's trust is very important for the e-tailer to reduce the level of perceived risk ex ante (Jarvenpaa and Tractinsky, 1999). Since consumers cannot see the outcomes of their shopping process ahead of time, they will judge an e-tailer based on cues that they encounter as they shop. For example, a consumer who may encounter a site that has broken links or grammatically incorrect language may well lose confidence in the ability of that e-tailer to deliver the necessary products on time.

Of course, this is not a "new" issue. Many scholars in marketing, psychology, and economics have studied how a firm can send signals to the consumer in order to reduce their perception of risk. For example, Boulding and Kirmani (1993) have shown that providing product warranties enhances consumers perception of unobservable product quality. Consumers reason that since the firm is willing to stand behind its product, it must be of a higher quality. Similarly, starting with Nelson (1974), economists have argued that consumers who observe high levels of advertising expenditure by firms perceive it as a signal of product commitment. The reasoning is that the firm must believe in the product to advertise it that heavily. Kirmani (1990) provides experimental evidence that this holds to a point beyond which consumers begin to argue that the excessive advertising is a signal that something is wrong.

In the e-tailing context, firms can attempt to reduce the perceived risk of consumers in many ways. For example, companies can obtain seals of approval from trusted third parties and display them prominently on their Web site (Palmer, Bailey, and Faraj, 2000). An example of such a seal is that offered by TrustE, an industry-sponsored non-profit organization focused on improving privacy practices in firms. The organization allows firms who meet certain requirements to post its seal on their Web site. Consumers who see the seal can then be assured that privacy will be ensured. Others have argued that the usability of the site is viewed as a strong signal of the competence of the firm (For example, see Jakob Nielsen's work at www.useit.com). Consumers who encounter confusing displays, poor instructions and "trust-busters" (e.g. broken or missing links, crashing sites - a term coined by Hoffman and Novak, 1998) will likely leave with a poor impression of the e-tailer.

E-tailing firms has scarce resources. Hence, they need to know which factors work in boosting customer confidence and which factors do not. Our paper is an effort to answer this question. Our focus is on identifying the causal antecedents to consumer confidence in e-tailers. Specifically, using secondary data, we study how site characteristics (e.g. site layout, online resources), assurances from intermediaries (e.g. TrustE privacy seal, BBB Online reliability seal) and firm characteristics (e.g. size, pre-Web experience) affect consumer confidence in the site.

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Brief literature review

Scholars are increasingly becoming interested in the issues presented in this paper. For example, Sultan, Urban, and Weinberg (2000) conducted a study to identify the dimensions underlying consumer trust in a Web site. Based on a survey of students in an e-commerce course, they identified five factors - professional/friendly appearance, navigational ease, brand, privacy, and transaction fulfillment. Similarly, Chen and Wells (1999) report a new scale to measure consumers' attitudes towards a Web site. They report three underlying dimensions to this scale - Entertainment, Informativeness, and Organization.

Others have studied how specific aspects of the Web experience affects attitudes towards the site and behavior. Dellaert and Kahn (1999) report that the delay of loading information can negatively affect evaluation of Web sites. Menon and Kahn (2000) study how the sequence of products or experiences viewed on one particular visit affects consumer attitudes and behavior. Danaher and Wilson (2000) show that the brand equity of the Web site has a significant impact on consumer attitudes.

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Conceptual framework

We develop a conceptual framework identifying the antecedents to customer confidence in an e-tailer.

The usability of an e-tailer's site has a major impact on shopping behavior (Nielsen, 2000). Since switching costs are low, consumers that encounter a poorly-designed site are very likely to switch (Nielsen and Norman, 2000). Moreover, improvements in the usability of a site can lead to dramatic improvements in the sales of the firm [3]. Two of the five factors described in Sultan, Urban, and Weinberg (2000) - appearance and navigational ease - relate to the usability of a Web site. Similarly, Chen and Wells (2000) also report that organization of information on a Web site affects consumer attitudes.

Kim and Yoo (2000) present an interesting study in the context of a electronic shopping mall. They show that the link structure significantly affects the shopping experience of consumers. Specifically, they show that different link structures can lead to different levels of cognitive convenience and emotional shopping pleasure.

Hence, we hypothesize that -

Hypothesis 1: The greater the ease of use, the greater the confidence of the customer in the Web site.

The current consensus in the marketing literature is that firms must attempt to build long-term relationships with consumers rather than focusing on each transactions (McKenna, 1991; Sheth and Parvatiyar, 1995). The idea is to understand the long-term value of the customer and allocate resources in accordance with these values (Day, 2000). The emphasis is on retaining customers rather than obtaining new customers (McGahan and Ghemawat, 1994).

A closely related idea is one-on-one marketing, which proposes thinking about a segment of size one (Peppers and Rogers, 1993; Pine, Victor, and Boynton, 1993). Given the new capabilities of addressing each individual (Blattberg and Deighton, 1991) the idea is to customize a product in accordance with the needs of a consumer.

This suggests that e-tailers who provide relationship-enhancing services will be viewed more positively. Based on this, we hypothesize that -

Hypothesis 2: The greater the relationship services offered by an e-tailer, the greater the confidence level of the customer.

The consumer will place greater confidence in an e-tailer who provides services that enhance the shopping experience. Consider an apparel e-tailer who offers a 3-D image of a dress that the consumer can view to get a fuller perspective. This is bound to enhance the confidence of the customer in the site. Hence, we propose that -

Hypothesis 3: The greater the resources offered to enhance the shopping experience, the greater the customer confidence.

In an influential book, Hagel and Singer (1999) introduce the term "infomediary". These are seen as a class of intermediaries who help consumers navigate through the clutter on the Internet. Examples of infomediaries include Bizrate.com and Gomez.com which rate Web sites so that consumers can identify the best sites to shop from. Other infomediaries include, for example, TrustE. This site audits Web sites' privacy policies and allows those that pass to display a seal that acts as a trust-building device with consumers. Similarly, the Better Business Bureau Online provides two seals - a reliability seal and a privacy seal. The latter works in a way similar to the TrustE seal. The reliability seal indicates that the bureau has verified that the site exists and the site is willing to follow the bureau's dispute resolution system.

Palmer, Bailey, and Faraj (2000) have examined the prevalence and effectiveness of seals from trusted third parties. They find that the use of seals increases with increases in the embededness of the sites. They do not examine the impact of seals on consumers.

Based on this, we hypothesize that -

Hypothesis 4: Assurances from trusted third parties will lead to increase in customer confidence.

A great deal of literature in marketing chronicles the so-called "double jeopardy" effect (Ehrenberg, Goodhart, and Barwise, 1990; Barnard and Ehrenberg, 1990). These studies describe the multiple advantages enjoyed by large brands (i.e., brands that are bigger in terms of market share). For example, larger brands not only have more consumers, but also more loyal consumers. Moreover, this effect has been shown to exist with respect to consumer attitudes as well (Farr and Hollis, 1997). Hence, we hypothesize that -

Hypothesis 5: The larger the e-tailer, the greater the customer confidence.

Firms who have had pre-Web experience are likely to be more familiar to consumers. As a result of this increased name awareness, consumers are expected to have greater confidence in their brand names (Keller, 1993). A prominent example of this includes the "clicks and mortar" firms who operate both bricks and mortar firms as well as online stores (Pottruck and Pearce, 2000). But, this also includes firms who have operated catalogs, home shopping networks, etc. Hence, we propose that -

Hypothesis 6: Consumers will be more confident with firms with pre-Web experience in retailing.

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Sample characteristics

Our sample was obtained from a respected infomediary - www.gomez.com. As indicated earlier, gomez is in the business of rating Web sites so that consumers (or anybody else for that matter) can see how different Web sites stack up against one another. We obtained information for four variables - Customer Confidence, On-site Resources, Relationship Services, and Ease of Use from gomez' Internet ScorecardTM methodology. This scorecard is put together using a mix of consumer surveys and expert judgement and is updated every quarter.

Two features of this data are important to note. First, gomez.com rates all firms that meet a minimum standard of service. This allows us to obtain data from all important e-tailers in any given category. This is a unique advantage of this dataset. Data provided by other infomediaries such as bizrate.com are only for companies have to join for a fee. Therefore, in our dataset, we have companies with a wide range of abilities rather than a self-selected, relatively homogenous sample. Second, gomez makes all efforts to standardize the measurement of variables across categories. As a result, comparison across categories becomes simple and meaningful.

Our sample consisted of 283 e-tailers who were drawn from 16 different categories as shown in Table 1. All data reported here was collected on 28 June 2000.

Table 1: Description of E-Tailers in Sample

Number Category Description Frequency Percent
1 Apparel 23 8.1
2 Books 15 5.3
3 General Merchandise 9 3.2
4 Gifts and Flowers 17 6.0
5 Golf 13 4.6
6 Music 19 6.7
7 Sporting Goods 20 7.1
8 Toys 17 6.0
9 Videos 22 7.8
10 Computers 31 11.0
11 Electronics 19 6.7
12 Furniture 17 6.0
13 Groceries 12 4.2
14 Home Furnishings 23 8.1
15 Home Improvement 11 3.9
16 Pet Supplies 15 5.3

TOTAL 283 100.0

We only chose firms engaged in business-to-consumer commerce selling a tangible product rather than a service [4]. These categories are meaningful since they relate closely to the top-selling categories in business-to-consumer commerce. This is clear from the data presented in Table 2. This information is drawn from a report by the NPD Group on the top categories of business-to-consumer commerce during February to April 2000. Hence, our sample is a good representation of the universe of e-tailers.

Table 2: Top Selling Categories in Business-to-Consumer E-Commerce,
February to April 2000
(Source: NPD Group. Excludes all services.)

Category Spending, $US Millions
Computers/Software 1,560
Books/Music/Video 827
Apparel 675
Health and Beauty Aids 399
Consumer Electronics 342
Office Supplies 336
Flowers/Gifts/Jewelry 302
Toys/Video Games 256
Grocery 255
Furniture 221
Prescription 118
Footwear 108

Our sample was a good mix of large and small sites. For example, prominent e-tailers such as Amazon.com, BUY.com and JCPenney.com were featured. In addition, several smaller e-tailers were also represented. Overall, 17.3% of e-tailers in our sample were featured on the Media Metrix Top 500 sites list.

There was some duplication across categories. For example, Amazon.com is featured on the list for books as well as music, toys, etc. We retained this format rather than eliminating duplicates. This made sense to us since customers are likely to judge a site differently for each category. Hence, just because I have confidence in Amazon.com when it comes to books, it does not mean I will have the same confidence with their toy department.

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Regression analysis

In the regression analysis, the store was the unit of analysis. The dependent variable in our regression analysis was customer confidence (CUSTCONF). This was measured on a scale of 0 to 10 where 0 stood for low confidence and 10 stood for high confidence. This was obtained from gomez.com. In their own words, "The leaders in this category operate highly reliable Web sites, maintain knowledgeable and accessible customer service organizations, and provide quality and security guarantees".

The independent variables used in the regression can be placed into three categories: site characteristics (mainly from gomez.com), trust-related variables, and finally, category dummy variables.

Data for three variables were obtained from gomez.com - On-site resources (indicated as ONSITERE), relationship services (RELSERV), and ease of use (EASEOUSE). All variables were on a 0-10 scale where 0 indicated the low point and 10 the high point. It is best to present the detailed description of each variable as available on the Web site of gomez -

1. On-Site Resources:

The top firms in this category not only bring a wide range of product and services and information onto the Web, but provide depth to these products and services through a full range of electronic account forms, transactions, tools, and information look-up. Roughly 30 to 50 criteria points including:

  • Availability of specific products.
  • Ability to transact in each product online.
  • Ability to seek service requests online.

2. Relationship Services:

Firms build electronic relationships through personalization, by enabling customers to make service requests and inquiries online and through programs and perks that build customer loyalty and a sense of community. Roughly 30 to 50 criteria points are assessed, including:

  • Online help, tutorials, glossary and FAQs.
  • Advice.
  • Personalization of data.
  • Ability to customize a site.
  • Re-use of customer data to facilitate future transactions.
  • Support of business and personal needs such as tax reporting or repeated-buying.
  • Frequent buyer incentives.

3. Ease of Use:

The Web site of a top firm in this category boasts a consistent and intuitive layout with tightly integrated content and functionality, useful demos and extensive online help. Roughly 30 to 50 criteria points are assessed, including:

  • Demonstrations of functionality.
  • Simplicity of account opening and transaction process.
  • Consistency of design and navigation.
  • Adherence to proper user interaction principles.
  • Integration of data providing efficient access to information commonly accessed by consumers.

We were somewhat concerned that there may be minimal variability in these variables which may limit our results. However, this did not turn out to be the case. Table 3 provides the descriptive statistics of all variables obtained from gomez.com. From this table, it is clear that there was sufficient dispersion in all variables. For example, the RELSERV variable ranged from a minimum of 0 to 9.40. Since gomez rates a wide variety of sites, this is to be expected.

Table 3: Descriptive Statistics of Variables Obtained from Gomez.com

Variable N Mean Standard Deviation Minimum Maximum
CUSTCONF 283 6.38 1.28 2.82 9.50
EASEOUSE 283 6.66 1.41 1.70 9.79
ONSITERE 283 4.89 1.55 0.49 8.82
RELSERV 283 4.12 1.99 0.00 9.40

In addition to these variables, we included a dummy for the size of the company (MM500). This was computed by checking if the site was listed on Media Metrix' Top 500 site list. This list is compiled from a panel of users; it is well respected in the industry and has been featured in several academic studies. This variable was coded as 1 if the site was present on the list and 0 otherwise.

There is an increasing school of thought that feels that customers have greater confidence in sites that had previously run businesses. Companies that ran brick and mortar stores are referred to as click and mortar firms. In addition, companies who ran catalogs (e.g. L.L.Bean) or home shopping networks (e.g. iQVC) are now established as e-tailers. Hence, we carefully went through the Web site of each e-tailer to determine if they had retailing experience prior to the Internet. This variable (NONWEB) was coded as 1 if they did and 0 otherwise.

Next, we included two trust-related variables. Scholars have pointed out that consumers' confidence in an e-tailer will increase if they see seals from trusted third parties. Hence, we included dummy variables from two such parties. First, we considered the Better Business Bureau's reliability seal (BBBREL). This is given to firms who agree to participate in the Bureau's complaint-handling system [5]. Second, we included information about the TrustE seal(TRUSTE). This is given to parties who agree to formulate a privacy policy that describes their practices of handling customer information [6]. Both variables were coded as 1 if the seal was present and 0 otherwise.

Finally, we included several dummy variables indicating the category in which the e-tailer offered products. These were as follows -

APPAREL=1 if e-tailer sells apparel, 0 otherwise,

BOOK=1 if e-tailer sells books, 0 otherwise,

GENMER=1 if e-tailer sells general merchandise, 0 otherwise,

GIFTFLOW=1 if e-tailer sells gifts and flowers, 0 otherwise,

GOLF=1 if e-tailer sells golf merchandise, 0 otherwise,

MUSIC=1 if e-tailer sells music, 0 otherwise,

SPORT=1 if e-tailer sells sporting goods, 0 otherwise,

TOY=1 if e-tailer sells toys, 0 otherwise,

VIDEO=1 if e-tailer sells videos, 0 otherwise,

COMPU=1 if e-tailer sells computers, 0 otherwise,

ELECT=1 if e-tailer sells electronics, 0 otherwise,

FURNI=1 if e-tailer sells furniture, 0 otherwise,

GROCE=1 if e-tailer sells groceries, 0 otherwise,

HOMEF= 1 if e-tailer sells home furnishings, 0 otherwise, and

HOMEIM=1 if e-tailer sells home improvement items, 0 otherwise.

We present results from several models -

Model 1

CUSTCONF= INTERCEPT + b1 ONSITERE + b2 RELSERV + b3 EASEOUSE + e

Model 2

CUSTCONF= INTERCEPT +b4 MM500 + b5 NONWEB + b6 BBBREL + b7 TRUSTE + e

Model 3

CUSTCONF= INTERCEPT + b1 ONSITERE + b2 RELSERV + b3 EASEOUSE + b4 MM500

b5 NONWEB + b6 BBBREL + b7 TRUSTE + e

Model 4

CUSTCONF= INTERCEPT + b1 ONSITERE + b2 RELSERV + b3 EASEOUSE + b4 MM500

b5 NONWEB + b6 BBBREL + b7 TRUSTE + b8 APPAREL + b9 BOOK + b10 GENMER +

b11 GIFTFLOW + b12 GOLF + b13 MUSIC + b14 SPORT + b15 TOY + b16 VIDEO +

b17 COMPU + b18 ELECT + b19 FURNI + b20 GROCE + b21 HOMEF + b22 HOMEIM + e

All four models were simple OLS models. In all cases, e stands for the usual error term.

Hypotheses 1-6 suggest that coefficients b1, b2b7 will be positive.

Since we were concerned about multicolinearity, variance inflation factors were computed for all regressions reported here. None of them exceeded 3. Since 10 is the allowed limit, this indicates that multicolinearity was not a big problem here.

The results are shown in Table 4 below. The significant terms are highlighted.

Table 4: Results from Regression Analysis

Model 1

Model 2

Model 3

Model 4

Independent Variables Coefficients P Value Coefficients P Value Coefficients P Value Coefficients P Value
INTERCEPT 4.4047 0.0001 6.2896 0.0001 4.1816 0.0001 3.9261 0.0001
ONSITERE 0.1741 0.0027

-

-

0.1614 0.0052 0.1544 0.0118
RELSERV 0.0713 0.1241

-

-

0.0699 0.1327 0.0486 0.3389
EASEOUSE 0.1781 0.0026

-

-

0.1578 0.0086 0.2149 0.0006
MM500

-

-

0.5649 0.0043 0.2458 0.1920 0.3103 0.1076
NONWEB

-

-

-0.2552 0.0874 -0.0850 0.5444 -0.1030 0.4784
BBBREL

-

-

0.2318 0.2582 0.0229 0.9053 -0.0050 0.9807
TRUSTE

-

-

0.6244 0.0054 0.5001 0.0165 0.4215 0.0448
APPAREL

-

-

-

-

-

-

0.4596 0.2435
BOOK

-

-

-

-

-

-

-0.6080 0.1464
GENMER

-

-

-

-

-

-

0.4301 0.3766
GIFTFLOW

-

-

-

-

-

-

0.5241 0.1983
GOLF

-

-

-

-

-

-

0.5163 0.2405
MUSIC

-

-

-

-

-

-

-0.4860 0.2207
SPORT

-

-

-

-

-

-

0.00003 0.9999
TOY

-

-

-

-

-

-

0.0397 0.9253
VIDEO

-

-

-

-

-

-

-0.5360 0.1786
COMPU

-

-

-

-

-

-

-0.4010 0.2667
ELECT

-

-

-

-

-

-

-0.3050 0.4446
FURNI

-

-

-

-

-

-

0.7276 0.0729
GROCE

-

-

-

-

-

-

0.2174 0.6334
HOMEF

-

-

-

-

-

-

0.1527 0.6886
HOMEIM

-

-

-

-

-

-

0.1425 0.7637

 

 

F-Value F(3,279)=21.076 F(4,278)=4.898 F(7,275)=10.216 F(22,260)=5.234
P value 0.0001 0.0008 0.0001 0.0001
Adj. R2 0.176 0.0524 0.1862 0.2483

Hypothesis 1 indicates that the greater the ease of use, the greater the customer confidence. Hence, this predicts that the EASEOUSE coefficient will be positive and significant. This is strongly supported. In Models 1, 3 and 4, the coefficient is consistently positive.

Hypothesis 2 states that the greater the relationship services offered by the e-tailer, the greater the confidence. This was not supported. While the RELSERVE coefficient was positive in Models 1, 3 and 4, it was insignificant.

Hypothesis 3 indicates that the greater the resources offered to enhance the shopping experience, the greater the confidence. Once again, this was strongly supported. The ONSITERE coefficient was positive and significant in Models 1, 3 and 4.

Hypothesis 4 suggests that we will find positive coefficients for BBBREL and TRUSTE in Models 2, 3 and 4. We received mixed support for this. The TRUSTE coefficient was positive and significant for all three models. However, the BBBREL coefficient was positive, but insiginificant in all cases. This may have been because individuals are more concerned with privacy issues rather than if the site has agreed to a dispute resolution process as laid out by the BBB.

Hypothesis 5 states that the larger the e-tailer, the greater the customer confidence. This suggests a positive coefficient for the MM500 variable. Once again, we received mixed support for this hypothesis. While the MM500 variable was positive and significant in Model 2, it was insignificant in Models 3 and 4.

Finally, Hypothesis 6 states that customers will have greater confidence in firms that have had retailing experience prior to the Web. This suggests a positive and significant coefficient for the NONWEB variable. We did not obtain support for this hypothesis. The coefficient was negative and weakly significant for Model 2 and insignificant for Models 3 and 4.

After going over our sample carefully, we found that a lot of e-tailers did have retailing experience prior to the Web. But, this experience was confined to one particular region or to a few stores. These firms did not necessarily have the name recognition of the national retailers. An example of this is MC Sports. It has an e-tailing operation as well as stores in five states in the U.S. Midwest. Perhaps only looking at stores with a national presence would have led to a positive coefficient here.

In sum, we observed strong support for Hypotheses 1 and 3. We did not observe support for Hypotheses 2 and 6. We observed mixed support for Hypotheses 4 and 5.

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Conclusion and discussion

In this paper, we have developed and tested a conceptual framework identifying the antecedents to customer confidence in an e-tailer's site. Our main findings are that customer confidence is influenced by the ease of use, presence of on-site shopping resources, and the presence of a TrustE seal. Larger sites may have a slight edge.

While we are able to make certain interesting and relevant observations, our study suffers from an excessive reliance on secondary data from one infomediary. While there are definite advantages to the way this data is collected (see sample characteristics section), we do not know how exactly the data is put together. Future studies must replicate our findings using consumer judgements.

As business to consumer commerce on the Internet grows, gaining and keeping customer confidence will be a major managerial challenge.End of article

 

About the Author

Sandeep Krishnamurthy is Assistant Professor of Marketing in the Business Administration Program at the University of Washington in Bothell, Wash. His current research interests include P2P business models, online communities, permission marketing, viral marketing, personalization, spamming, and privacy. He teaches E-Marketing and Internet Business Model Lab to undergrads and E-Commerce to graduate students. He invites you to start an e-conversation with him at the following e-mail address.
E-mail: sandeep@u.washington.edu
Web: http://faculty.washington.edu/sandeep/vita

Notes

1. www.emarketer.com

2. The only exceptions to this would be specialized services such as electronic brokerage or errand-running services such as kozmo.com.

3. http://www.useit.com/alertbox/20000611.html

4. We would argue that products and services represent inherently different propositions. For example, in electronic brokerage services, an individual can buy or sell a share with a click without having to wait for "delivery".

5. For a longer description of the criteria, see http://www.bbbonline.com/businesses/reliability/standards.html

6. More detail is available at http://www.truste.org/webpublishers/pub_principles.html

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S. Menon and B.E. Kahn, 2000. "Cross-Category Effects of Induced Arousal and Pleasure on the Internet Shopping Experience," INFORMS Marketing Science Conference 2000; see also http://www-marketing.wharton.upenn.edu/ideas/pdf/97-006.pdf

P. Nelson, 1974. "Advertising as Information," Journal of Political Economy, volume 82, number 4, pp. 729-754. http://dx.doi.org/10.1086/260231

J. Nielsen, 2000. Designing Web Usability: The Practice of Simplicity. Indianapolis: New Riders.

J.W. Palmer, J.P. Bailey, and S. Faraj, 2000. "The Role of Intermediaries in the Development of Trust on the WWW: The Use and Prominence of Trusted Third Parties and Privacy Statements," Journal of Computer Mediated Communication, volume 5, number 3 (March).

D.S. Pottruck and T. Pearce, 2000. Clicks and Mortar: Passion-Driven Growth in an Internet-Driven World. San Francisco: Jossey-Bass.


Editorial history

Paper received 29 November 2000; accepted 31 December 2000.


Copyright ©2001, First Monday

An Empirical Study of the Causal Antecedents of Customer Confidence in E-Tailers by Sandeep Krishnamurthy
First Monday, Volume 6, Number 1 - 8 January 2001
http://www.firstmonday.dk/ojs/index.php/fm/article/view/825/734





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