The Effect of a firm’s Privacy Practices on Customer Online Trust
Juhee Kwon
Krannert Graduate School of Management
Purdue University
403 W. State Street, West Lafayette, IN 47907
juheek@purdue.edu
Jackie Rees
Krannert Graduate School of Management
Purdue University
403 W. State Street, West Lafayette, IN 47907
jrees@purdue.edu
1
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
2
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
2
See http://www.infoworld.com/article/08/01/08/Sears-sued-over-privacy-breach_1.html
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 Ecommerce” (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 widelyaccepted 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
policies on their websites to announce a strategic mechanism that conveys the trustworthy image
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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 highesttraffic 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
prices, but also by performance of confidentiality, privacy procedural fairness and data integrity.
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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 trustsatisfaction 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
restricted by a firm’s performance for a quality of service or product. However, the rapid growth
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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 ecommerce trust in terms of their propensity to privacy concerns (Kim & Benbasat, 2006; Kim,
2008; Miyazaki, 2008; Pavlou, Liang, & Xue, 2007). They demonstrated that the propensity to
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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
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
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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
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
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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
A Firm’s Operational Obligations: Perceived Values and Qualities
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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.
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
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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
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
13
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-ofmouth. 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.
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
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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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
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
15
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
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
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
16
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
Contact Information
Behavioral Information
biographical,
and financial
Biographical Information
information
Financial Information
The Required Information
Name, E-mail address, Mailing address, Telephone numbers
Browsing habits
Gender, Age, Education, Income, Personal interests, Hobbies
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
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
(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 250 current
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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
20
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
21
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
1. Personal
Information
1.00
2.Secondary Use
-0.06
1.00
3.Third Parties’
Data collection
0.12
0.18**
1.00
4.Offline Channels
0.32*
-0.01
0.18**
1.00
5.Online Service
Performance
-0.22*
0.08
-0.14
-0.07
1.00
6.Firm Size
0.22*
0.03
0.18**
0.60*
0.01
6
Tolerance
0.85
0.95
0.90
0.59
0.92
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 ( x q ) is the “structural model”, and the other links among dictators ( y p ) 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 x1 , x 2 ,...x q ,
Structural Model:
   1 x1  2 x2   3 x3   4 x4   5 x5   6 x6  
Considering the variables:
x1
The amount of data collection
x2
Secondary Use
22
(1)
x3
Information sharing with third parties
x4
The existence of offline channels
x5
Online service Performance
x6
Firm size
Then, the latent variable (  ) determines a set of observable endogenous indicators, y1 ,.. y p as
the equations (2) and (3),
Measurement Model:
y1  1   1
(2)
y2  2   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 (χ2 =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).
23
Figure 2. The Path Diagram
Table 5. Standardized Parameter Estimates, t-values and Summary of Results
Structural Path
Standardized
Coefficient
Personal Information →Trust (γ1)
-0.90
Secondary Use →Trust (γ2)
-0.41
Third Parties’ Data collection →Trust (γ3)
-0.47
An Offline Channel →Trust (γ4)
-1.07
Online Service Performance →Trust (γ5)
0.54
Firm Size →Trust (γ6)
0.52
Trust→ Customer Satisfaction (λ1)
0.48
Trust→ Customer Participation (λ2)
0.22
Chi-square with 5 degrees of freedom
Goodness of fit (GFI)
Adjusted goodness of fit (AGFI)
Root mean square error of approximation(RMSEA)
Comparative fit index (CFI)
*p<.01,**p<.05
24
=
=
=
=
=
19.31 (p=.00168)
0.97
0.77
0.14
0.93
t-value
Hypothesis
-6.76*
H1
-3.41*
H2
-3.78*
H3
-6.68*
H4
4.4*
H5
3.52*
H6
≈0
H7
4.34*
H8
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
follows procedural fairness for privacy, they prefer to go to its offline shops or make a call for
25
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
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.
26
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29
APPENDIX A. Sample
Industry
Company
Internet Brokerage
Fidelity Investments
Charles Schwab Corporation, The
TD Ameritrade Holding Corporation
E*TRADE Financial Corporation
MSNBC.com (NBC, Microsoft Corporation)
ABCNEWS.com (The Walt Disney Company)
NYTimes.com (The New York Times Company)
CNN.com (Time Warner Inc.)
Google Inc.
Yahoo! Inc.
MSN (Microsoft Corporation)
Ask.com (IAC/InterActiveCorp)
AOL LLC (Time Warner Inc.)
AltaVista Company
Microsoft Corporation
Amazon.com, Inc.
Newegg Inc
Netflix, Inc.
eBay Inc.
Overstock.com, Inc.
Buy.com Inc.
barnesandnoble.com llc
1- 800- FLOWERS.COM, Inc.
uBid.com Holdings, Inc.
Expedia, Inc.
Orbitz Worldwide, Inc. (Cendant Corporation)
priceline.com, Incorporated
Travelocity.com L.P. (Sabre Holdings Corporation)
Barnes & Noble, Inc.
Borders Group, Inc.
Costco Wholesale Corporation
Office Depot, Inc.
Staples, Inc.
SAM'S CLUB (Wal- Mart Stores, Inc.)
Office Max, Incorporated
The Gap, Inc.
Lowe's Companies, Inc.
The TJX Companies, Inc.
Best Buy Co., Inc.
Circuit City Stores, Inc.
Home Depot, Inc.
Internet News & Information
Internet Portals/Search Engines
Internet Retail
Internet Travel
Retail Stores
(Click and Mortar)
30
APPENDIX A. Sample - continued
Industry
Company
Health & Personal Care Stores
(Click and Mortar)
Walgreen Co.
CVS/Caremark Corporation
Rite Aid Corporation
DIRECTV Group, Inc., The
DISH Network
Cox Communications, Inc.
Time Warner Cable Inc.
Charter Communications, Inc.
Comcast Corporation
Nordstrom, Inc.
Kohl's Corporation
Dollar General Corporation
Target 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 Corporation
AT&T Inc.
Qwest Communications International Inc.
Cox Communications, Inc.
Embarq Corporation
Verizon Communications Inc.
Verizon Wireless (Cellco Partnership)
AT&T Mobility LLC
T- Mobile USA, Inc. (Deutsche Telekom AG)
Sprint Nextel Corporation
Cable & Satellite TV
Department & Discount Stores
(Click and Mortar)
Telecommunication
31
APPEDIX B. Privacy Dimensions
No
I
Description
Notice
What information they collect
How they collect it
How they use it
What About Cookies and Action Tags
What About Third-Party Advertisers and Links to Other Websites
Whether to allow third parties to use cookies or web beacon, or sharing
information for advertising
Conditions of Use, Notices, and Revisions
II
Choice
What Choices Do consumers Have?
The detail level to explain cookies and action tags
Internal secondary uses
External secondary uses
III
Access
whether they Offer consumers reasonable access to the information a website
has collected about them
How to Protect the security of the information they collect from consumers
IV
Security
Internal/managerial procedure to prevent unauthorized access to customer
information
Seal 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;
Data Storage
Measures 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
VII
Unsolicited email; unsolicited email from third parties;
32
33