The Effect of a firm’s Privacy Practices on Customer Online Trust

Juhee KwonKrannert Graduate School of Management

Purdue University403 W. State Street, West Lafayette, IN 47907

juheek@purdue.edu

Jackie ReesKrannert Graduate School of Management

Purdue University403 W. State Street, West Lafayette, IN 47907

jrees@purdue.edu

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Abstract

The paper estimates the relationship between a firm’s privacy practices and customer online

trust by means of a structural equation approach, and the trade-off between privacy concerns and

other perceived values of a firm. Then, it also identifies how online trust affects on customer

satisfaction and willingness to participate in the electronic market.

The empirical test results suggest that a firm’s privacy practices negatively affects its customer

online trust, while customers’ perceived values for a firm have positive effect on their trust.

Furthermore, customer online trust has a significant effect on their participating in the electronic

market. However, the results do not support the effect of online trust on customer satisfaction.

From this evidence, we can conclude that even though privacy practices are currently very

concerned by privacy advocators and government, the perceived values from the quality of a

product and service or price, dilute privacy concerns on customer satisfaction on a particular

firm, despite harming customer participating in its online service. Therefore, if customers cannot

believe a firm maintains the reasonable level of privacy practices, they prefer to go to its offline

shops or service centers, rather than to utilize its more accessible online service. This finding is

relevant to click and mortar firms which overtly penetrate into an online channel or more activate

it, as well as pure play firms which maintain or acquire market sharing. However, privacy

concerns seem to be more critical for pure players, since customers cannot have any alternative

channels for their products or services, as customers can do with click and mortar players.

Keywords: Privacy, Trust, Satisfaction, Participation, Structural Equation Model

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1. Introduction

Internet technology (IT) has presented a new framework for customer relationships and

transactions. It has been possible to map patterns of consumer behavior by getting close to the

consumer over the Internet (Bessen, 1993). At the same time, many firms implemented customer

relationship management (CRM) systems to capture customers’ information and adopted

marketing techniques, e.g. direct and interactive marketing and customization. Accordingly, IT

has encouraged firms to take advantage of this newly acquired their customers’ personal

information. However, some firms have crossed the line in utilizing customers’ information by

passing the information on to business partners, spammers, telemarketers, and direct mailers, and

then provoked protests and discontent. For instance, Sears faces a class-action lawsuit after

making the purchase history of its customers public on its business partner, Managemyhome.com

web site1. The lawsuit wants Sears to determine whether Managemyhome.com was misused by

criminals. Also, Charter Communications, one of the nation's largest Internet service providers,

announced that it planned to enhance its service by installing software on its Internet lines to map

its customers' browsing behavior to sell ads tailored to customers' interests in May 2008.

However, it created an immediate protest from customers and the plan was cancelled2.

Currently, most of firms have the ability to exploit a name, an email address, personal likeness

or documents for their own profit or gain without the customer’s consent. However, the misuse

and abuse of personal information hurts customers in various ways, whether its unsolicited

emails, credit card frauds or identity thefts.

In particular, the electronic market has been even more concerned about than the traditional

market. Eastlick et al. (2006) suggested privacy concerns influenced purchase intent with strong

1 See http://www.infoworld.com/article/08/01/08/Sears-sued-over-privacy-breach_1.html2 See http://www.slate.com/id/2198119/

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negative effects on customer trust (Eastlick, Lotz, & Warrington, 2006). Wang and Emurian

(2005) showeded privacy concerns build “a most formidable barrier to people engaging in E-

commerce” (Wang & Emurian, 2005). Several empirical studies have shown privacy concerns

significantly deteriorated customers’ willingness to participate in E-commerce over the Internet,

due to its significant influence on building trust (Garbarino & Johnson, 1999; Sirdeshmukh,

Singh, & Sabol, 2002). Indeed, since the electronic market involves high uncertainty, limited

legal protection, low switching costs, and numerous competitors, acquiring customer trust about

a firm’s fulfillment of privacy must be one of the most important competitive advantages, as well

as a determinant of customer satisfaction (Luo, 2002; Selnes, 1998).

Therefore, it has become more important for the electronic market to resolve privacy concern

problems and understand how a firm’s privacy practices affect customer’s privacy concerns.

While the personal information usage has become a competitive necessity to meeting customers’

needs in the competitive e-business environment, it lays a heavy burden on companies to ensure

adequate privacy protection (Bowie & Jamal, 2006).

Even though privacy issues and trust have been studies for many years throughout psychology,

marketing, and information systems literature, there have been very few studies dedicated to

empirically examining their relationship. The purpose of our paper is to study how firms’ various

privacy practices affect customer trust, which is closely related to satisfaction and participation

to the E-commerce. We also explore the effect of firm’s performance perceived values, e.g. a

firm’s performance, which moderates the influences from the perceived privacy risks from firms’

privacy practices. The results demonstrate the effects of privacy concerns and firms’ other values

on trust. Furthermore, we show how differently privacy practices work on pure-play and click

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and mortar businesses across industries. This study can give firms the insights into how to set up

their practices for customer trust and willingness to invest in a long-term business relationship.

We structure the rest of the paper as follows: In section 2, the background literature is reviewed

and then, we develop the hypotheses in section 3. In section 4, we discuss the research

methodology. Section 5 resents the result and section 6 discusses the implications from the

results. Lastly, we conclude our study and suggest opportunities for the future work.

2. Theoretical Background

This study employs two major streams of literature. One stream researches the privacy

procedural fairness, which firms try to implement for building customer trust. The other studies

the relationship among privacy, trust, satisfaction and customers’ willingness to participate in the

electronic market.

Firms’ Procedural Fairness for Privacy and Trust

Procedural fairness can be defined as a customer’s perception that a particular activity, in

which he/she is involved by a relationship with a firm, is fairly conducted. Many researchers

have been suggested that customers’ perceptions on a firm’s fulfillment for their privacy could

motivate them to establish a long-term relationship, closely related to customer trust and loyalty.

Culnan and Armstrong (1999) found that when customers are told explicitly that a company will

observe fair information procedures, they are more willing to disclose their personal information

and to allow the company to subsequently use the information to develop target marketing

(Culnan & Armstrong, 1999). In 1998, the Federal Trade Commission (FTC) issued the widely-

accepted fair information practice principles of Notice, Choice, Access, and Security for online

privacy. According to the FTC report (2003), more than 90% of firms have posted privacy

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policies on their websites to announce a strategic mechanism that conveys the trustworthy image

and they comply with privacy policies in their own self-interest. Some survey research examined

firms’ privacy policies and showed that more than 80% of random samples adhere to the

principles embodied in the Fair Information Practices (Ryker, Lafleur, McManis, & Cox, 2002;

Schwaig, Kane, & Storey, 2006). Furthermore, in terms of the compliance with privacy policies

statements under a self-regulation system, Jamal, Maier, and Sunder (2003) investigated the

actual practices of firms against their own posted privacy policies. They selected the highest-

traffic 100 websites in the U.S. and identified which websites used their own and/or third party

cookies to collect personal information. Then, they also compared the observed behavior to the

relevant disclosure of cookie usage in the privacy policies. The results showed that 97 percent of

the total sample disclosed their privacy policy and 88 percent explain what cookies are and the

kind of data they collect with cookies (Jamal, Maier, & Sunder, 2003, 2005). Other research also

suggests that a self-reported guarantee of compliance with industry standards is an effective way of

increasing customer trust (Pennington, Wilcox, & Grover, 2003; Ranganathan & Ganapathy, 2002).

Consequently, a firm’s privacy practices from its policy statement are contractual commitment to

customers outlining how their personal information will be treated. If a firm’s privacy policy can

successfully address procedural fairness to abate privacy concerns and fair information practices

are observed, customers are more willing to trust and continue in a relationship with a firm.

Trust, Satisfaction, and Participation in the Electronic Market

Many researchers have demonstrated that customers are reluctant to provide personal

information or participate in electronic market transactions due to a lack of customer trust in

either the ability or the intent of firms (Sipior, Ward, & Rongione, 2003). In this study, Customer

Trust is defined as a subjective belief that the provider will fulfill its obligations on both

transactions and operations. It has not only been influenced by firms’ performance like quality or

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prices, but also by performance of confidentiality, privacy procedural fairness

and data integrity. In terms of privacy concerns, customer trust results in a customer’s

willingness to participate in the electronic market and disclose his/her personal information

there. (Ba & Pavlou, 2002; Lee & Turban, 2000; Suh & Han, 2003). If customers

cannot believe that their transactions and data are handled safely and securely, they try to switch

providers. In particular, the more competitive industry becomes, the more information firms

require with various purposes such as personalized services or direct marketing. However, it can

make customers feel that private information has been violated, while a firm believes it provides

better services to customers (Culnan & Armstrong, 1999).

Furthermore, in consumer marketing research, the causal relationship between trust and satisfaction has

been discussed for many years. While some research claimed trust leads to satisfaction in the exchange

relationship between buyers and providers(Armstrong & Yee, 2001), some others hypothesized a positive

flow from satisfaction to trust (Ganesan, 1994; Geyskens, Steenkamp, & Kumar, 1999). Since the trust-

satisfaction relationship is developed through repeated interactions, satisfaction is considered as one of

major indicators of trust. This study employs Expectation-Confirmation theory (ECT) to explain

the trust-satisfaction relationship as a background theory (Oliver, 1980). This concept has been the

popular approach for measuring customer satisfaction in marketing and information system

literature (Bhattacherjee, 2001; Garbarino & Johnson, 1999; Kopalle & Lehmann, 2001;

McKinney, Yoon, & Zahedi, 2002; Susarla, Barua, & Whinston, 2003). ECT framework state

that customer satisfaction results from a comparison of expectation, which are a set of trusts

about desired attributes of a product or service. This effect is mediated through positive or

negative disconfirmation between customer trust and a firm’s actual practice. If a firm

outperforms a customer’s expectation, post-purchase satisfaction will result. Otherwise, the

customer is likely to be dissatisfied. Generally, customer satisfaction is considered to be mainly

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restricted by a firm’s performance for a quality of service or product. However, the rapid growth

of IT and the increasingly competitive environment have forced companies to collect a great

amount of personal information and map the patterns of customer behaviors. This phenomenon

has made customer satisfaction more complex. Therefore, this paper accommodates a firm’s

privacy practice into this concept of expectation and confirmation theory.

3. Research Model and Hypotheses

Figure 1 shows the conceptual framework of this paper based on Expectation-Confirmation

theory. If consumers do not trust a firm to which they disclosed their information, they raise

privacy concerns and then their concerns undermine the level of satisfaction from the quality of a

firm’s products or services (Mithas, Krishnan, & Fornell, 2005). Currently, most of firms realize

that while personal information has value for building successful consumer relationship, trust

would be much more valuable for it.

Figure 1: The Conceptual Framework

A variety of models have proposed that consumer heterogeneity plays a vital role in e-

commerce trust in terms of their propensity to privacy concerns (Kim & Benbasat, 2006; Kim,

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2008; Miyazaki, 2008; Pavlou, Liang, & Xue, 2007). They demonstrated that the

propensity to privacy concerns is likely influenced by consumers’ awareness

of privacy, their experiences about some situations with risks, cultural

background, and so on. Our study is different from the previous research in

sense that we focus on how firms’ privacy practices affect the overall

customer satisfaction and willingness to participate in an online services.

This perspective shed the light on a firm’s decisions on its privacy policies

considering the trade-off between privacy risks and the effectiveness of its

operational performance. Since more and more consumers have become anxious about

privacy, it has been critical to identify the effect privacy concerns which consumers have across

most of industries where click and mortar or pure play business model have dominated.

A Firm’s Transactional Obligations: Privacy Practices

Consumers’ privacy concerns are closely related to consumer’s trust which plays a key role in

the electronic market that involves high uncertainty and lack of legal protection (Luo, 2002).

Since most of firms across various industries provide various online services such as customer

service, Internet shopping, billing and company information, their websites play an important

role in capturing information about customers. Their services are collecting amounts of personal

information and the need for excessive and increasing collection habits is cause for concern. As

we mentioned earlier, although more than 90% of firms posted their privacy policy on the

websites based on the procedural fairness recommended by the FTC, firms have fairly different

privacy practices on their policy statement. Furthermore, under self-regulation system, firms’

posted privacy policies effectively work as their strategies to build consumers’ trust as well as to

defend themselves against privacy lawsuit. We investigate firms’ privacy policies from privacy

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statements and then analyze how the different commitment levels affect consumer satisfaction

and which parts have more significant influence on that. This study has more focused on

collecting personal information, the secondary use of that information and the allowance of third

parties’ data collection, in terms of information sharing.

Most of the time, the elements, required by a firm, do not give a reasonable spectrum of

choices for what information consumer provides to use the services. Firms normally make

customers fill in all of the required form fields, and otherwise they cannot use firms’ services at

all. When consumers have no choice but to use the service, they are placed in an

uncompromising position. Then, more and more consumers do not have enough confidence in

firms to protect their privacy. While much of the research on trust has focused on measures of

consumers’ beliefs, it has not considered the levels of personal information requested by the web

sites. There is a great of variety in the types of personal information requested by web sites.

Some sites require extensive personal information to be allowed to access a web site. At the

other extreme, some web sites permits customers to conduct transactions based on a limited

amount of information. It seems likely that the type of information requested could affect beliefs

concerning risk and thus the willingness or intention s of consumers to engage in the relationship

with a firm. This paper examines the inherent risk that is associated with the levels of personal

information required from consumers.

Hypothesis 1: Requiring larger amounts of personal information decreases customer trust in

an online service.

Once an individual discloses personal information to a firm’s website, she/he usually has little

or no control on how the information could be used. Personal information can be internally or

externally exploited with other purposes, such as behavioral marketing, promotion or data

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mining. Concerns regarding the secondary use of information loom large consumers from

engaging in online relationship exchanges. Control over secondary use of information is likely to

be a critical point on privacy concerns. Over 80% of online consumers simply do not want the

web sites where they visit to share their information to other businesses (Mabley, 2000).

Hypothesis 2: A firm’s allowance for the secondary use of personal information decreases

customer trust in an online service

Furthermore, most of firms allow third-parties to capture consumers’ information by using

cookies or web beacons. “Third-party cookies” is defined as cookies placed by a third party not

directly visited by the consumer. Generally, they are sanctioned by the visited web site to build

consumer profiles by the third-party organization for various purposes such as targeted

marketing or advertising (Lavin, 2006). For example, Google places a cookie when a user clicks

on paid keyword advertising. When the user goes the page of the site that sponsored the key

word, the cookie sends information about this back to the Google servers and the sponsored site.

Furthermore, third-parties collect personal information by placing web bugs or beacons, “clear

GIFs’ that are only one pixel by one pixel in size which essentially makes them invisible to the

customer. Web sites can relay user traffic information to third-parties using Web bugs or

beacons, invisible pieces of code as well as contain links leading to external domains with

privacy practices different from those of the original sites where consumer visited. Although web

browsers are now equipped to provide consumers with the ability to reject or delete cookies in

accordance with their privacy preferences, many consumers do not take advantage of these

functions (Ha, Inkpen, Shaar, & Hdeib, 2006; Linn, 2005).

Hypothesis 3: A firm’s allowance for third parties’ use of cookies and web beacons decreases

customer trust in an online service

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A Firm’s Operational Obligations: Perceived Values and Qualities

Customers’ perceptions about the value and quality of a firm can be affected by various

factors, such as brand reputation, advertisement, price, and the quality of a product or a service.

These perceptions depend on how a firm fulfills its operational obligations. This study

categorizes a firm’s operational obligations into 3 types – service channels with the end

customers, the quality of its online services for the end customers, and the firm size, which

represent a firm’s performance in terms of the front end of its business processes.

First, clearly firms could either be exclusively online (e.g., Amazon) or have both online and

brick and mortar presence (e.g., Wal-Mart). Customers’ perceived values of a firm might be

different between the exclusive online and both channels presence, since the reputation and trust

built for the brick and mortar business can be transferred to the online store. It is therefore

important to consider the online or offline presence of a firm as important issues in building

trust.

Hypothesis 4: The existence of offline channels affects customer trust in an online service.

Second, the quality of its online services should be considered as one of a firm’s operational

obligations in terms of a firm’s information privacy practices. Many firms have tried to improve

the quality of its online services, since the high quality of online services can encourage

customers to participate in the online services or e-commerce. Although the measure can be

captured by various factors, such as web usability, speed, and so on, the number of unique pages

viewed per user per day for a website, can be considered as an aggregate value, which implies

most of the above factors.

Hypothesis 5: The higher quality of a firm’s online services increases customer trust in an

online service.

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Lastly, a firm size represents its performance in terms of qualities and prices. The value of a

firm’s performance from its operations is measured by its sales and the quality of services. A

larger firm indicate that the firm has more customers and more likely follows through with

commitments made to its, because customers are more aware of a firm’s practices (Doney &

Cannon, 1997). On the other hand, a less trustworthy and more opportunistic firm would be

unable to build sales volume or large market share. Therefore, customers would rationally

determine that since larger firms would incur significant costs through untrustworthy behavior

than smaller firms, there is merit in trusting larger firms. Brand recognition is also an indicator of

a firm’s trustworthiness (Gommans, Krishnan, & Scheffold, 2001; Zhang & Zhang, 2005). A less

trustworthy company will not be able to be in business for a long time, especially in a highly

competitive e-business environment. In an exchange relationship, the professional reputation of a

firm serves as a hostage. If the firm begins to violate the consumer’s trust, the consumer quickly

lets it be known, throughout the network of friends, colleagues, and associates, that the firm is

disreputable (Luo, 2002). Many marketing literature considered a firm’s sales as the indicators of

its overall performance.

Hypothesis 6: Firm size positively affects customer trust in an online service.

The Indicators of Customer Trust

In the ETC framework, consumer satisfaction depends on the evaluation of the discrepancy

between expectation and a firm’s actual performance. Through this evaluation process, the level

of consumer satisfaction can be measured on a better than expected or worse than expected scale

(Oliver, 1993). Customer satisfaction and willingness to participate in the electronic market are

affected by their trust or belief about that a firm fulfills its operational obligation for the quality

of products or services as well as transactional obligation to protect their privacy. Despite the

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perceived privacy risks, consumers might decide to continue to have a relationship with the firm

due to the benefits provided by a firm. If the value of a firm’s performance from the quality or

price highly dilutes the perceived privacy risks, it can compensate for the loss of consumer trust

or belief about privacy. However, in the Internet environment, the importance of transactional

obligation has exponentially increased.

Under the ECT framework, expectation and confirmation are theorized as the determinants of

satisfaction. Customer’s expectation represents the prior experience including both experiential

and non-experiential information such as a firm’s offering, policy, advertisings and word-of-

mouth. This value can also reflect consumers’ anticipated behaviors, since it provides the

reference level for consumers to form evaluative judgments about what they expect to receive

from a firm. Particularly, the electronic market context does allow consumers to form their

expectation based on even more various direct or interactive relationships. So, Internet

consumer’ expectation can be extended to the belief that a firm fulfill their transactional

obligations which include protecting their information as well as selling. we integrate

Expectation-Confirmation theory (ECT) with customer satisfaction information from ACSI (The

America Consumer Satisfaction Index)3 that was tracked by the National Quality Research

Center (NQRC) at the University of Michigan (Anderson & Fornell, 2000; Fornell, Johnson,

Anderson, Cha, & Bryant, 1996). ACSI estimates consumer satisfaction based on the

confirmation between consumer’ expectation and experience as it is shown in Figure 1. The

expectation and experience are based on a firm’s trustworthiness, customization, and quality.

This concept has the same theoretical framework with an ECT framework. Therefore, our

integration maintains the consistency between both of the models.

3 See http://www.theacsi.org/, ACSI reports scores on a 0-100 scale at the national level. It also produces indexes for 10 economic sectors, 43 industries, and more than 200 companies. The measured companies, industries, and sectors are broadly representative of the U.S. economy serving American households.

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Besides, customer trust results in a consumer’s willingness to participate in the electronic

market. If customers cannot believe that a firm fulfills its obligations, they are not willing to

participate in a firm’s online services. This unwillingness results in the relatively lower traffic on

its website. Figure 2 shows the theoretical research framework of our study.

Hypothesis 7: Customer trust in an online service increases the overall customer satisfaction

on a firm.

Hypothesis 8: Customer trust on online service increases customers’ participation in a firm’s

online services or e-commerce market.

Figure 2. The Research Model

4. Research Methodology

Data Collection

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First, the sample consisted of 73 companies which are click and mortar or pure players across

10 industries (Appendix A). These firms can selected from the lists of firms for which the

National Quality Research Center (NQRC) at the University of Michigan, provide consumer

satisfaction index. Second, firms’ privacy practices for this study were collected from an

electronic copy of the privacy policies of the websites and investigated to find disclosure about

cookie usage and the use of third-party cookies. Third, we attempt to register users on firms’ web

sites and then the required information and the level of personalization are gathered. Fourth, the

other variables of a firm’s level such as a firm’s size and marketing expense are collected from

Compustat. Fifth, Customer satisfaction data are collected from ACSI (The American Consumer

Satisfaction Index)4 that was tracked by the National Quality Research Center (NQRC) at the

University of Michigan to obtain an archival measure of customer satisfaction for the firms.

Lastly, the traffics of a firm’s websites and the number of unique pages viewed per user per day

are gathered by Allex.com5. These data collections on key independent and dependent variables

from separate sources can avoid common method bias.

Personal Information (x1). We review the web sites of firms and learn that personal

information, requested by a firm, can be generally classified as contact, behavioral, biographical,

and financial information (Meinert, Peterson, Criswell, & Crossland, 2006). First, contact

information includes such items as e-mail address, name, mailing address, and telephone

numbers. This information can be applied for several purposes including creating mailing lists to

promote products, or services. However, it may also be shared with third parties. Second,

behavioral information includes clicks stream or transactional data through placing cookies. The

purposes of collecting behavioral information are to scrutinize customers' browsing habits such 4 See http://www.theacsi.org/, ACSI reports scores on a 0-100 scale at the national level. It also produces indexes for 10 economic sectors, 43 industries, and more than 200 companies. The measured companies, industries, and sectors are broadly representative of the U.S. economy serving American households. 5 See http://www.alexa.com/, Alexa.com provides web traffic information as a subsidiary of Amazon.com

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as what they looked at and where they went and then to sell lucrative products or services

tailored to customers' interests. For instance, if a firm saw that a customer had been reading lots

of auto reviews, it might show her/his interest in new cars. Third, biographical information

means demographic data such as gender, age, education, income, personal preferences, interests,

and hobbies. A firm may use biographical information to profile customers, target future

communications for marketing purposes, and customize web pages for individual customers.

Fourth, financial information includes credit card numbers and bank account numbers. Although

consumers are obviously reluctant to provide financial information, this information is often

viewed as necessary to complete an e-commerce transaction. We measure the type of the

requested personal information by investigating how many elements a firm requires in its

registration process.

Table 1. The type of personal information requested by a firm

Categories The Required Information

Contact Information

biographical, and financial

Name, E-mail address, Mailing address, Telephone numbers

Behavioral Information Browsing habits

Biographical Information Gender, Age, Education, Income, Personal interests, Hobbies

Financial Information Credit card numbers, Bank account

Secondary Use (x2). Firms’ privacy policy statements show that their practices in secondary

use are fairly different. Some firms declare that they might disclose personal information they

have collected for providing the information of new products which they or their business

partners will provide. The others provide opt-in or opt-out options for the sharing of any

sensitive personal information with their subsidiaries, affiliated companies or other businesses

partner. We divided this measure into internal and external sharing with the four values as it is

show in Table 2.

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Third party’ collecting data (x3). Firms show the different level of the restriction of third

party’s collecting data. A firm with a less restrictive policy might announce as followings, “You

may occasionally get cookies from our business partners (e.g., advertisers, tracking utilities) or

other third parties with links on the Websites. We have no control or access to these cookies. The

use of advertising cookies sent by third-party servers is standard in the Internet industry”

(Borders, 2008)6. This allowance can make other organization where customers do not visit

collect personal information. On the other hand, Microsoft tells, “We prohibit Web beacons on

our sites from being used by third parties to collect or access your personal information”6. We

constructed this measure as the four values as it is show in Table 2.

Table 2. The Secondary Use and Third Parties’ data collection

Value Internal/External Secondary Use

0 No allowance

1 Opt-in option

2 Opt-out option

3 Allowance

The Perceived Value of a firm. The perceived value of a firm can be measured by three

elements: service channels for the end customers, the quality of its online services for the end

customers, and the firm size. First, if a firm provides offline services for its end users, Offline

Channels (x4) is set as 1, and otherwise, it is as 0. Second, the quality of its online services for the end

customers can be measured by the number of unique pages (x5) viewed per user per day for this site. The

data were collected from www.alexa.com. Although the quality of its online services includes various

factors such as web usability, personalization, speed, and so on, these number of unique pages

viewed by user can represent the aggregate level of online service quality, in sense that web

6 See, http://privacy.microsoft.com/en-us/fullnotice.mspx, Microsoft’s privacy policy.

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users are more willing to participate in an online service when a firm’s website provide more

convenient and tailored services. Lastly, as an indicator of a firm’s trustworthiness, we can

consider a firm’s revenue(x6) which can be extracted from DATA 12 in Compustat. Table 3

shows the descriptive statistics of the variables.

Table 3. The Descriptive Statistics

Variable Mean Std Minimum Maximum

Personal Information 10.04 3.33 2.50 15.00

Secondary Use 1.12 1.44 0.00 3.00

Third Parties’ Data collection 2.92 0.36 1.00 3.00

Online Service Performance 5.32 2.81 1.76 15.45

Firm Size 23,302 44,254.32 7 344,896

Offline 0.77 0.43 0.00 1.00

Customer Satisfaction 73.99 6.71 54.67 88.00

Web Traffics 25,550 87,202 13 656,011

(Firm Size: million)

Customer Satisfaction (y1). We employ the ACSI measures as an indicator of a firm’s

customer satisfaction. The data have been used in several academic studies in the accounting and

marketing literature (Anderson, Fornell, & Mazvancheryl, 2004; Fornell et al., 1996). The ACSI

measures are cumulative and it is reasonable in the sense that our study considers consumer

satisfaction as the result of evaluating the discrepancy between expectations about privacy risks

and actual perceived performance, since the ACSI has been measured based on three

antecedents, which are consumer expectation, perceived quality and perceived value (Garbarino

& Johnson, 1999; Greenberg, Wong-On-Wing, & Lui, 2008; Selnes, 1998). For each firm, about

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250 current customers participated in the survey. Interviews came from 48 national probability

samples of households in the United Sates (Fornell et al., 1996).

Web Traffic (y2). As we mentioned before, customer trust results in a consumer’s willingness to

participate in the electronic market. Privacy and order fulfillment are the most influential

determinants of trust for sites in which both information risk and involvement are required and

results in customers’ participating in the sites (Bart, Shankar, Sultan, & Urban, 2005). This can be

measured by the web traffic of a firm’s web site. If a firm can convince customers to protect their

personal information, customers are more likely to participate in a firm’s online services by disclosing

their information. We collected the average of the web traffics for the last 3 months from www.

Alexa.com

5. Data Analysis and Results

Our model has a latent variable, customer trust as an endogenous variable. Customer trust is

supposed to be caused by a firm’s operational values as well as privacy practices which include

the amount of data collection, the secondary use of personal information, and Information

sharing with third parties. Then, the level of customer trust in an online service can be indicated

by customer satisfaction and participating in the electronic market. The structural equation model

can explain statistical relationships among latent (unobserved) and manifest (observed) variables.

Compared with the regression and the factor analysis, SEM is a relatively young tool. It explains

the variables on the unobservable variable.

In order to examine customer trust, we employ the Multiple Indicators and Multiple Causes

(MIMIC) model, a particular type of SEM models. This approach considers several causes and

several indicators of the hidden variable (Joreskog & Goldberger, 1975). This means the latent has

the usual multiple indicators, but in addition it is also caused by additional observed variables. Frey and

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Weckhanneman (1984) have been the first to consider the size of hidden economy as an

unobservable variable (Frey & Weckhanneman, 1984). They employed the MIMIC model of

Jöreskog and Goldberger (1975) to explain hidden variables in the economic field.

The eight hypotheses presented earlier were tested using the structural equation modeling

approach, also performed using LISREL, which was developed in 1970s by Karl Jöreskog and

Dag Sörbom as a statistical software package used in structural equation modeling (Jöreskog &

Sörbom, 1993). Latent variable modeling has become a popular research tool in the behavioral

marketing since a general framework for specifying structural equation models was introduced

(Jarvis, MacKenzie, & Podsakoff, 2003). Marketing and consumer behavior researchers have

focused on causal modeling for data analysis (Siguaw, Simpson, & Baker, 1998; Srinivasan &

Ratchford, 1991). The latent variables were linearly determined by a set of observable exogenous

causes and linearly determined a set of observable endogenous indicators.

Diagrammatically, the model has the usual arrows from the latent, trust to its indicators of satisfaction

and participation. In addition, as Figure 2 shows, there are rectangles representing observed causal

variables with arrows to the latent, since they are exogenous variables (Bollen & Lennox, 1991; Fornell &

Bookstein, 1982). Unlike the general reflective model, it would be entirely consistent for formative

indicators to be completely uncorrelated in case that a latent construct is represented by mutually

exclusive types of behavior. Furthermore, internal consistency reliability should not be used to evaluate

the adequacy of this formative model. In addition, multicollinearity among indicators can be a significant

problem for measurement model parameters estimates (Jarvis et al., 2003).

Multicollinearity Test

Table 4 displays the correlation matrix. The correlations among exogenous variables show low

values. However, firm size and offline channels seem high. Therefore, we conducted a formal

multicollinearity test with the regression. The multicollinearity diagnostic returns a tolerance

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value of around 0.6, which is above the common cutoff threshold of 0.1 (Hair, Tatham,

Anderson, & Black, 2005). So, multicollinearity is not a concern for this model.

Table 4. Correlation Matrix of the Variables and Tolerance Value

1 2 3 4 5 6 Tolerance

1.Personal Information 1.00 0.85

2.Secondary Use -0.06 1.00 0.95

3.Third Parties’ Data collection 0.12 0.18** 1.00 0.90

4.Offline Channels 0.32* -0.01 0.18** 1.00 0.59

5.Online Service Performance -0.22* 0.08 -0.14 -0.07 1.00 0.92

6.Firm Size 0.22* 0.03 0.18** 0.60* 0.01 1.00 0.62

*p<.01, **p<.05; all other correlations are insignificant.

For the LISREL, the equations system with the relationships among the latent variable ( ) and

the causes ( ) is the “structural model”, and the other links among dictators ( ) and customer

trust are called as the “measurement model”. An analytical representation of the model is below.

The customer trust ( ) is determined by the following equation with a set of observable

exogenous causes ,

Structural Model:

(1)

Considering the variables:

x1 The amount of data collection

x2 Secondary Use

x3 Information sharing with third parties

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x4 The existence of offline channels

x5 Online service Performance

x6 Firm size

Then, the latent variable ( ) determines a set of observable endogenous indicators, as

the equations (2) and (3),

Measurement Model:

(2)

(3)

Considering the variables:

y1 Customer Satisfaction Index

y2 Web Traffic

Path analysis through LISREL 8.7 (Jöreskog and Sörbom, 1993) was used to test the

hypotheses presented by Figure 1. The correlation matrices of the constructs appear in Table 4.

The analysis of this model presented in Figure 2 resulted in a fit to the data (χ 2 =19.31[df=5],

p=.00168; RMSEA = .141, Comparative Fit Index (CFI) = 0.93). Table 5 presents the

standardized path coefficients and the t-values associated with the estimates. The amount of

personal information, required by a firm, has a significant negative effect on customer trust (H1;

γ1= –.90, p <.01). Both of a firm’s secondary use of personal information and allowance to third

parties’ data collection were supported with the negative effects of γ2 = –.41 (p <.01) and γ3 =

–.47 (p <.01), respectively. In terms of the perceived values of a firm, an online service

performance and a firm size positively affect customer trust in a firm’s online services with of γ4

=.54 (p <.01) and γ5 = .52 (p <.01), respectively. Surprisingly, the existence of offline channels

has a negative effect on customer trust with γ6 = –1.07 (p <.01).

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Figure 2. The Path Diagram

Table 5. Standardized Parameter Estimates, t-values and Summary of Results

Structural Path Standardized Coefficient

t-value Hypothesis

Personal Information →Trust (γ1)-0.90 -6.76* H1

Secondary Use →Trust (γ2)-0.41 -3.41* H2

Third Parties’ Data collection →Trust (γ3)-0.47 -3.78* H3

An Offline Channel →Trust (γ4)-1.07 -6.68* H4

Online Service Performance →Trust (γ5)0.54 4.4* H5

Firm Size →Trust (γ6)0.52 3.52* H6

Trust→ Customer Satisfaction (λ1) 0.48 ≈ 0 H7

Trust→ Customer Participation (λ2) 0.22 4.34* H8

Chi-square with 5 degrees of freedomGoodness of fit (GFI)Adjusted goodness of fit (AGFI)Root mean square error of approximation(RMSEA)Comparative fit index (CFI)

= 19.31 (p=.00168)= 0.97= 0.77= 0.14= 0.93

*p<.01,**p<.05

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On the other hand, customer trust has a positive effect on customer satisfaction with λ1 = 0.48,

but it is not significant. However, the result shows that customer trust significantly encourages

customers to participate in the electronic market with λ2 = 0.22 (p <.01).

6. Discussion and Conclusions

Although firms and customers have recognized the importance of privacy on customer online

trust, closely related to customer satisfaction and participation in the electronic market, empirical

research that involves examining their effects is still in its infancy. This study models and tests

potential effect of a firm’s privacy practices on customer satisfaction and participation through

customer online trust using a MIMIC approach of the SEM model, which is substantially

employed by the emerging stream of literature on consumer behavioral research (Jarvis et al.,

2003). The empirical test results suggest that a firm’s privacy practices negatively affects its

customers’ trust in its online services, while customers’ perceived values for a firm have positive

effect on their trust. However, the negative effect of offline existence is not consistent with

Meinert et al.’s work which suggested that a firm’s reputation and trust from the offline channels

can be transferred to the online store (Meinert et al., 2006). With this result, we can conclude that

pure online companies are supposed to more securely protect customers’ privacy. Furthermore,

customer trust has a significant effect on their participating in the electronic market, while the

results do not support the effect of customer trust on its satisfaction. At this moment, we can

extract an insightful conclusion based the insignificant effect of online trust on customer

satisfaction. Currently, even though privacy practices are very concerned by privacy advocators

and government, the perceived values from the quality of a product and service or price, dilute

privacy concerns on customer satisfaction. In other words, if customers cannot believe a firm

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follows procedural fairness for privacy, they prefer to go to its offline shops or make a call for

their additional services, rather than to utilize its web services. These findings are important for

several reasons. The results of the study indicate that a firm’s privacy practices can affect

directly customer trust, which encourages them to participate in the electronic market. This

suggests that if a firm wants to activate its online channel, it should set up the reasonable privacy

practices and explicitly mention them to make customers aware to its privacy practices. This

finding is relevant to click and mortar firms which overtly penetrate into an online channel or

more activate it, as well as pure play firms which maintain or acquire market sharing. However,

privacy concerns seem to be more critical for pure players, since customers cannot have any

alternative channels for their products or services, as customers can do with click and mortar

players.

In conclusion, we contribute to privacy issues in two major respects. First, the study provides

substantive support for previous findings and additional insight about the interrelationship

between privacy and trust. Second, and most important, this paper provides clear evidence that

the high standard of a firm’s privacy practices are important to attract customers into its web site.

This evidence is especially timely for firms seeking a means to go toward online channel with

various reasons such as cost-efficiency or various service offerings. Although the findings from

this study are significant to privacy research, this study has some limitations. First, we assume

consumers are homogeneous across companies or industries. Second, a firm is supposed to

comply with its policy statement. Lastly, our sample has the limited number of observations

which are around 140 firms (currently 73 firms). For the future work, we need to more

discompose consumer satisfaction values into expectation and confirmation. Identifying among

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trust, privacy, expectation and confirmation can give more specified insights for a firm’s policy

strategy. Also, customer heterogeneity across companies or industries needs to be considered.

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References

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APPENDIX A. Sample

Industry CompanyInternet Brokerage Fidelity Investments

Charles Schwab Corporation, TheTD Ameritrade Holding Corporation

E*TRADE Financial CorporationInternet News & Information MSNBC.com (NBC, Microsoft Corporation)

ABCNEWS.com (The Walt Disney Company)NYTimes.com (The New York Times Company)

CNN.com (Time Warner Inc.)Internet Portals/Search Engines Google Inc.

Yahoo! Inc.MSN (Microsoft Corporation)

Ask.com (IAC/InterActiveCorp)AOL LLC (Time Warner Inc.)

AltaVista CompanyMicrosoft Corporation

Internet Retail Amazon.com, Inc.Newegg IncNetflix, Inc.

eBay Inc.Overstock.com, Inc.

Buy.com Inc.barnesandnoble.com llc

1- 800- FLOWERS.COM, Inc.uBid.com Holdings, Inc.

Internet Travel Expedia, Inc.Orbitz Worldwide, Inc. (Cendant Corporation)

priceline.com, IncorporatedTravelocity.com L.P. (Sabre Holdings Corporation)

Retail Stores (Click and Mortar)

Barnes & Noble, Inc.Borders Group, Inc.

Costco Wholesale CorporationOffice Depot, Inc.

Staples, Inc.SAM'S CLUB (Wal- Mart Stores, Inc.)

Office Max, IncorporatedThe Gap, Inc.

Lowe's Companies, Inc.The TJX Companies, Inc.

Best Buy Co., Inc.Circuit City Stores, Inc.

Home Depot, Inc.

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APPENDIX A. Sample - continued

Industry CompanyHealth & Personal Care Stores

(Click and Mortar)Walgreen Co.

CVS/Caremark CorporationRite Aid Corporation

Cable & Satellite TV DIRECTV Group, Inc., TheDISH Network

Cox Communications, Inc.Time Warner Cable Inc.

Charter Communications, Inc.Comcast Corporation

Department & Discount Stores(Click and Mortar)

Nordstrom, Inc.Kohl's Corporation

Dollar General CorporationTarget Corporation

J.C. Penney Corporation, Inc.Dillard's, Inc.Macy's, Inc.

Sears, Roebuck and Co.Army and Air Force Exchange Service (AAFES)

Sears Holding Corporation (includes Kmart)Wal- Mart Stores, Inc.

Kmart CorporationTelecommunication AT&T Inc.

Qwest Communications International Inc.Cox Communications, Inc.

Embarq CorporationVerizon Communications Inc.

Verizon Wireless (Cellco Partnership)AT&T Mobility LLC

T- Mobile USA, Inc. (Deutsche Telekom AG)Sprint Nextel Corporation

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APPEDIX B. Privacy Dimensions

No   Description

I Notice

What information they collectHow they collect itHow they use itWhat About Cookies and Action TagsWhat About Third-Party Advertisers and Links to Other Websites

Whether to allow third parties to use cookies or web beacon, or sharing information for advertisingConditions of Use, Notices, and Revisions

II Choice

What Choices Do consumers Have?The detail level to explain cookies and action tagsInternal secondary uses External secondary uses

III Access whether they Offer consumers reasonable access to the information a website has collected about them

IV Security

How to Protect the security of the information they collect from consumersInternal/managerial procedure to prevent unauthorized access to customer informationSeal Program

V Data Collection

Collection and storage of personally identifiable information;Collection of aggregate information; users' ability to view and update data profiles; collection of user data via surveys; sweepstakes used to gather customer data; obtaining user information from other sources; storage and usage of email addresses from inquiries; cookies; information on disablement of cookies; information on consequences of disabling cookies; Web beacons;

VI Third-Party Data Collection

Types of data collected by third parties; third-party cookies or Web beacons; privacy agreement with third parties collecting data; opt-out of third-party data collection;

VII Data StorageMeasures taken to ensure secure offline storage of data; measures taken to prevent unauthorized employee access; users' ability to delete PII; records of PII kept after user deletes PII;

VIII Data Sharing

Privacy agreements with business agents receiving PII; sharing of aggregate information with affiliates; sharing of PII with affiliates; sharing of aggregate information with third parties other than business agents; sharing of PII with third parties other than business sweepstakes/surveys;

XI Marketing Communication Unsolicited email; unsolicited email from third parties;

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