An Empirical Evaluation of Information Features
and the Willingness to be Profiled Online for Personalization
Neveen I. Farag
University of Michigan Business School
Ann Arbor, MI 48105
nfarag@umich.edu
M. S. Krishnan
Michael R. and Mary Kay Hallman e-Business Fellow
Associate Professor and Area Chair of Computer Information Systems
University of Michigan Business School
Ann Arbor, MI 48105
mskrish@umich.edu
March 3, 2003
Please do not cite, quote, or distribute without permission of the authors.
Financial support for this study was provided in part by the Michael R. and Mary
Kay Hallman fellowship, the Marcy Maguire fellowship, the Information Systems
Executive Forum at the University of Michigan Business School, and the Summer
Research Opportunity Program at the University of Michigan.
An Empirical Evaluation of Information Features and the Willingness to be Profiled
Online for Personalization
Abstract
As customer information becomes a critical success factor in the global
networked economy, privacy is of strategic importance. Firms are able to use
information to improve customer service and offer personalized features. By
understanding the factors affecting consumer willingness to share information, managers
can tailor their firm’s e-business offerings to encourage consumers to partake in
personalization activities. This study hypothesized that firms can address the risks of
online profiling, and therefore encourage consumers to share their information, by
making consumers aware of firm procedures. Consumer concern over the information
collected and how it will be used (INFO_USE), should affect consumers’ willingness to be
profiled online. Customers will allow their activities to be monitored and used to create
consumer profiles for business use if they are privy to the profile information collected by
the firm. Knowledge of firm procedures should increase consumer perceived control,
thereby decreasing consumer perceived risk, and eventually increasing participation in
customer relationship activities, including personalized service and advertising. Based on
a survey of over 500 online consumers, our hypothesis was supported through our finding
that customers who rate knowledge of firm procedures as a “high” concern, are also less
willing to allow personal information to be tracked and stored in a profile used for
personalized service and advertising. The results of this study should help managers
tailor their firm’s online service offerings, and consequently, maximize useful and
beneficial information collection across various consumer-segments.
1
Introduction
In today’s networked global economy, the ability to collect, analyze, and respond
to user information is of growing importance. To survive, companies depend on vast
quantities of information to build rapport with existing customers and attract new
business (Culnan and Armstrong 1999). As the ease and availability of e-business
reduces face-to-face interactions, firms must use consumer information to attempt to offer
personalized service that will increase value, and consequently, consumer loyalty. As
Peter Weill and Michael R. Vitale state in their book, Place to Space: Migrating to eBusiness Models, “Information technology (IT) infrastructure and the information it
contains, particularly customer information, will be a critical success factor for all ebusiness initiatives, thus raising the stakes for the management of the firm's IT
investments and assets.” However, implicit in the collection of consumer information
lies a concern for consumer privacy; information privacy is one of the most important
issues facing management practice (Safire, 2002; Mason, 1986). If managers are not
careful, their firms may be the victims of consumer backlash for overstepping the bounds
of expected information practices. In order to encourage consumers to participate in
online information collection, a firm may therefore implement policies and features that
addresses a user’s information privacy concerns.
The importance of IT related privacy issues is also seen through the growing
global and governmental trend of increasing corporate privacy regulation (Dresner 1996,
Franklin 1996, Hendricks 1998). In addition, a pending Congressional privacy bill
would require US companies to give customers digital access to personal information that
firms possess (Thibodeau, 2002). This bill is an example of a growing trend towards
2
attempting to inform the consumer. Informing the consumer of firm procedures differs
from exercising fair information practices. A central element of fair information
practices is the ability of individuals to remove their names from mailing lists (Culnan
and Armstrong 1999). A central element of informing consumers of firm procedures,
however, is the ability of consumers to actually see what of their personal information a
company possesses. Thus, informing the consumer deals more directly with allowing
consumers’ access to the type of information a firm possesses and how it plans to use it.
The objective of this paper is to address the question: How can a firm increase
consumer willingness to be profiled online for personalized offerings? More specifically,
two research questions guide this study: 1) How does perceived control, achieved through
knowledge of information and firm procedures, affect consumer willingness to be
profiled online for personalized offerings? and, 2) How does consumer willingness to be
profiled online differ across personalized service versus personalized advertising? The
major contribution of this research provides empirical evidence that companies can
garner increased consumer participation in online information sharing activities by
providing consumers with knowledge of and access to information and usage procedures.
Through increased consumer participation, firms should then be able to utilize the
customer information for competitive advantage.
In the next section, we review and discuss prior literature. In section three, we
discuss the theoretical model and hypotheses of this paper. In section four, we explain
the data and measurement. In section five, we present the analysis and results. In section
six, we discuss the results and their managerial implications. We then conclude the paper
with directions for future research.
3
2) Prior Literature
Public opinion surveys show that many citizens are quite concerned about threats
to their information privacy (Equifax 1996; Westin 1997; Harris and Westin, 1998).
Several of the expressed privacy concerns centered on how firms collect and use personal
data. Previous work has found that how a firm handles buyer-collected personal
information offline affects the relationship between the firm and its consumers (Hoffman,
Novak, & Peralta, 1999). Privacy issues online are arguably not any different than
privacy issues offline. However, due largely to the increased amount of information
online, and the fear of identity theft (FTC Report, 1999), consumer comfort with online
profiling remains low. In fact, a Business Week (2000) survey showed that 63% of those
surveyed were not comfortable with anonymous online profiling; 89% were not
comfortable with identified online profiling. While privacy is an issue, there is also a
case for how firms can use online information to benefit consumers; we next address this
stream of research.
Research has shown that companies may be able to use profile information to
increase consumer value and move from mass merchandising to personalized service
(Pine 1993; Farag and VanAlstyne 2000; Farag and Krishnan, 2002). Blattberg and
Deighton (1991) have shown that when detailed consumer-information is collected, firms
are able to engage in relationship marketing and target offers more accurately based on a
customer’s specific interests. Organizations can therefore gain a competitive advantage
from collecting and using transaction data effectively (Farag and Krishnan, 2002; Glazer
4
1991). However, the potential downside of information collection, if implemented
improperly, is that it may actually raise a user’s privacy concerns rather than create value
(Bloom et. al 1994). Hence, the issue of online information collection has received the
attention of researchers, practitioners, and policy makers alike.
Several theories on the link between firm information practices and individual
behavior are emerging. For example, Milburg, Smith and Burke (2000) developed a
conceptual model examining corporate management of personal data, regulatory
approaches to information privacy, and consumer reactions across cultures. Culnan
(1993) tested an exploratory model that explains consumer attitude toward some direct
marketing practices offline. With this model, Culnan showed that public attitudes
towards privacy are likely to vary based on dimensions of control. Smith (1994)
developed a model to explain corporate approaches to information privacy policymaking. Hoffman et al. (1999) suggested that two larger dimensions govern privacy
concerns: environmental control and secondary use of information control. Laufer et al.
(1976) illustrated that perceived control over various uses of information results in less
consumer concern over privacy invasions. In addition, Stone et. al (1983) showed the
more a user values privacy, or rather, the more concerned about privacy, the less control
the consumer perceives to have over personal information. Stone and Stone (1990)
developed a model for information flows and physical/social structure in work
environments based on expectancy theory.
The above stream of research is grounded in the basic definition of privacy found
in psychology literature. Privacy is defined as the ability of the individual to control the
5
terms under which personal information is acquired and used (Westin 1967).
“Information privacy,” then, refers to “the ability of the individual to personally control
information about one’s self” (Stone, et al., 1983). Hence, it may be interpreted from
this definition that one way to decrease the level of perceived privacy risk for the online
consumer is to increase his or her level of control over personal information. Consistent
with this line of work, Culnan and Armstrong (1999) identify control as one of the two
main information privacy concerns that occur with firm handling of personal information.
The other concern being secondary use, where consumer information provided for one
purpose may be reused for another purpose without consent (Culnan, 1993; Smith et al.,
1996; Godwin, 1991; Foxman and Kilcoyne, 1993). In this paper, we relate the perceived
risk of consumers, measured through perceived importance of control, to their
willingness to be profiled online.
Knowledge has been shown to be a determinant of perceived control (Wortman
1975, Azjen and Driver 1991, Armitage and Conner 1999). In addition, knowledge as a
mechanism for control has been examined in prior organizational literature (e.g. Sohn
1994). Previous research has shown that Internet usage is constrained for some adults
due to the perceived need for more knowledge and understanding of the medium (Klobas
and Clyde 2000). In this paper, we extend the idea of knowledge as a control mechanism
towards improving consumer comfort. While consumer comfort with a company is
important in any setting, the threat of consumer discomfort is heightened in the Internet
setting, where consumers can easily “flame” a company directly by electronic mail or in
Internet newsgroups (Bies and Tripp 1996).
6
In this paper, we attempt to test consumer decisions to share information within
the framework of expectancy theory. Control is defined as a set of mechanisms designed
to motivate individuals to work in such a way that desired objectives are achieved (Kirsch
1996, Jaworksi 1988). In the case of online customer relationship management, a firm’s
desired outcome is for consumers to participate in online profiling. A company’s
challenge, then, is to mitigate the perceived risks of being profiled online, such that they
can provide consumers the desired outcome of personalized service and personalized
advertising. A classic conceptual model used to understand how individuals make
decisions regarding various behavioral alternatives in the face of perceived risks is
grounded in the expectancy theory model of motivation (Vroom, 1964). The central
theme of expectancy theory is that outcomes drive individual behavior. Thus, individuals
will chose between alternatives by evaluating the outcomes, or the anticipated future
consequences of the alternatives. While previous research has suggested that issues of
informational control are essential in creating a favorable consumer predisposition toward
contributing information to online firms, (Stewart and Segars, 2002), none have
examined the issue of control using the expectancy theory framework.
The contributions of this paper to the literature are as follows: 1) This paper adds
to our understanding of knowledge and perceived control in the online context; 2) This
paper examines importance of firm features towards decreasing consumer perceived risk,
and increasing consumer perceived control, across two contexts, advertising and service;
and, 3) While prior studies have examined likelihood of consumers to partake in online
personalization from purely the consumer characteristic standpoint (e.g. Chellappa and
Sin, 2002), this paper studies past consumer experiences in the context of privacy
7
concerns, along with consumer characteristics, that affect consumer perceived control;
and, therefore willingness to be profiled online. Culnan and Armstrong (1999) have
previously examined consumer willingness to share information, however they did so in
an offline setting, and focused on the use of procedural fairness, rather than information
access and usage awareness. Similarly, Phelps, Nowak, and Ferrell (2000) examine the
relationship between consumer purchase decisions and the amount of information control
given to consumers in the offline setting. Other studies have examined the likelihood of
consumers to partake in online personalization services from purely the consumercharacteristics standpoint. For example, Chellappa and Sin (2002) examine consumer
attributes, such as privacy concern and personalization value, and how such attributes
affect consumer likelihood of using personalization services. While we also include
consumer attributes, the main focus of our study is firm features that increase consumer
perceived control online, decrease consumer perceived risk, and thereby encourage
consumers to allow online profiling towards personalization. Thus, while control has
also been looked at as it relates to Internet privacy concerns (Dinev and Hart, 2002), the
association of consumer information features and consumer willingness to be profiled
online is yet to be examined. We, therefore, fill this gap, while also examining the effect
of such firm features across the contexts of personalized service and personalized
advertising.
3) Theoretical Model and Hypotheses
Consumer willingness to share information online involves the evaluation of the
outcomes of online profiling. Consumers are, therefore, faced with two alternatives, to
8
partake in online profiling, or to decline. A classic conceptual model used to understand
how individuals evaluate such alternatives, and their potential outcomes, is the
expectancy theory of motivation (Vroom, 1964). Our model is based on the central
premise of expectancy theory of how individual behavior is shaped.
Expectancy theory
states that three elements guide an individual’s evaluation process: 1) Expectancy, or the
belief that one’s actions will be followed by a particular outcome; 2) Instrumentality, or
the expectation that other outcomes will lead to the expected outcome; and 3) Valence, or
the attractiveness of the outcome to the individual. Figure 1 illustrates Expectancy
Theory. Perceived control over performance affects expectancy, or the perceived
probability that an action will be followed by a given outcome. For expectancy to be
high, individuals must believe that they have some degree of control over the expected
outcome. When individuals perceive that the outcome is beyond their ability to
influence, motivation to participate is low (Vroom 1964).
Motivation Force
Expectancy
Force Directing
Specific Actions
Perceived Probability
Action leads to
Outcome
=
*
Instrumentality
Valence
Perceived Probability
Outcomes will lead to
Expected Outcome
Value of Expected
Outcome to
Individual
*
Figure 1: Expectancy Theory
Expectancy theory was originally described by Vroom as follows:
n
Fi = ∑ ( EijV j I j )
j =1
Where:
Eij= The strength of the expectancy ( 0 ≤ Eij ≤ 1 ) that act i will be followed by outcome j.
Vj=The valence of outcome j
Ij= The cognized instrumentality of outcome j
This framework has been applied in different settings; while the elements of the
theory are widely accepted, researchers in multiple disciplines have also highlighted the
9
challenges of empirical validation. For example, in accounting, the theory has been
applied to audit staff performance; however, only weak support was found for the
model’s predictive power (Ferris, 1977). Thus, we do not explicitly test expectancy
theory, due to the significant problems of conceptualization and methodology associated
with specification of each of the elements of the theory, as several researchers have
pointed out (Connolly, 1976). Rather, we attempt to validate the effect of knowledge and
perceived control on online profiling, by focusing on elements of expectancy theory that
consumers can partake in to mitigate the risks associated with being profiled online.
In our setting, the outcome is measured in a single dimension related to the
experience of the customers after they have shared their personal information with the
firm. The outcomes the consumers may experience in return for the action of taking part
in online profiling range from useful personalization to information misuse, resulting in
email spam, or even worse, identity theft. Thus, the two extremes of the outcome are
useful personalization, or information misuse. The force, then, on the action of whether
the consumer should partake in online profiling or not is measured as willingness to
partake in online profiling.
Useful Personalization
Misuse of Information
The main independent variable we examine in our study, features a firm can
implement towards increased perceived control, is associated with Expectancy, the
likelihood that an action will be followed by the anticipated outcome. Thus, we are
examining factors that increase the likelihood for consumers that their information will be
used to produce an outcome of useful personalization. Note that the expectancy theory
10
inherently captures the tradeoff by individuals who surrender a measure of privacy in
exchange for some economic or social benefit of personalization. This tradeoff has been
directly studied offline as the “privacy calculus,” which measures the usage of personal
information versus the potential negative consequences of disseminating personal
information (Laufer and Wolfe, 1977; Milne and Gordon, 1993; Stone and Stone, 1990).
Our study examines this specific trade-off, which consumers make in the online setting,
and what firms can do to decrease consumer perceived risk.
Next we discuss the hypotheses that link perceived risk factors related to
information features, knowledge about firm privacy policy, consumer privacy concern,
and past privacy experience. Individual control affects expectancy, or the probability that
the action of sharing information will be followed by a given outcome (Vroom, 1964);
the greater such probability, the greater the force to partake in the action. Thus, we
believe that knowledge and perceived control online decrease consumer perceived risk of
online profiling, and therefore affect consumer willingness to partake in online profiling.
Hence, our hypotheses are aligned with the theory of the expectancy model.
Hypotheses
Information Features
Prior studies have identified that consumers are less likely to perceive a risk, and
therefore more willing to share information when consumers perceive the ability to
control future use of their information (Culnan and Armstrong, 1999; Bies, 1993; and
Stone and Stone, 1990). In addition, Expectancy theory postulates that control decreases
the perceived risk of partaking in an action by increasing the probability that a given
11
action will be followed by a given outcome. This increased likelihood then leads to an
increased force to of action towards online profiling. These findings may indicate that
the first step towards encouraging consumers to partake in online profiling is to increase
consumer perceived control. Firms may attempt to increase perceived control by
allowing consumers to understand the type of information a firm is collecting, and how
that information is being used. In this study, we examine “information features,” or
features firms implement to inform the consumer of firm procedures. Such features may
therefore increase consumer willingness to be profiled online. Firm implemented features
that allow consumers to view and control the use of their personal information should
effect the expectancy beliefs of consumers, or rather that their action of being profiled
online will lead to the desired outcome of useful personalization. Thus, we hypothesize:
consumers who rate importance of information features higher will be less willing to
partake in online personalization.
Hypothesis 1: The more important a consumer deems information-features, the
less willing they are to partake in online profiling.
Privacy Policy
One way firms attempt to address consumer perceived risks of online profiling is
by posting their privacy policy online. Privacy policies address the expectancy portion of
expectancy theory, or the expectation that an act will be followed by a given outcome.
The degree to which firm practices are formalized in written policies has an impact on
individuals' perceptions (Vroom, 1964). In addition, announcing a firm's privacy policy
has been shown to increase consumer perceived trust by allowing consumers to make
informed decisions about disclosing their personal information (Culnan, 1999; Hansen,
12
2000). By posting a privacy policy, firms are essentially telling consumers what outcome
to expect when the consumer partakes in the action of online profiling. Thus, by
increasing the probability of a positive outcome for the consumer, firms may use privacy
policies to increase consumer motivation to participate in online profiling. Consumers
that place a high importance on firm privacy policies, likely perceive a lower probability
of a desirable outcome, and therefore want written assurance. We then hypothesize that
the more importance a consumer places on online privacy policies, the less the consumer
will be willing to partake in online profiling.
Hypothesis 2: The more importance a consumer places on a privacy policy, the
less likely that the consumer will partake in online profiling.
Consumer Privacy Concern
Consumer concerns are affecting Internet commerce. A 1997 study revealed that
purchases via the Internet would receive a $6 billion boost by the year 2000 if consumers
believed their privacy wasn’t at stake during such transactions (Greene, 1997). From a
theoretical standpoint, personal values, such as privacy concerns, affect the Valence
portion of expectancy theory, or the attractiveness of the outcome to the individual.
Consumers with a higher level of privacy concern will likely perceive personalization
offerings to be of less value than consumers with a lower level of privacy concern. The
more a user views online privacy as a concern, the less motivation they will have to
partake in the action of online profiling. Prior research has shown that users that express
concern over their own privacy perceive little control over the use of their personal
information (Stone et. al 1983) and are likely less willing to share such information. We
test this finding in our own setting, and specifically in accordance with expectancy
13
theory. Thus, we hypothesize that a consumer’s increased concern for privacy is
associated with a lower desire to share their personal information, and therefore a
decreased force towards online profiling. Thus, we expect that greater privacy concern is
associated with less willingness to be profiled online.
Hypothesis 3: The greater a consumer’s general privacy concern level, the less
the consumer is willing to be profiled online.
Previous Privacy Invasion
Personal experiences guide behavior in activities that can be subjectively deemed
as privacy-related (Bates 1964). In addition, personal experiences cause a change in
privacy concern over an individual’s lifetime (Louis Harris and Associates, Inc. 1991).
Previous experiences are accounted for in the Expectancy Model through Instrumentality,
the expectation that other outcomes will lead to the expected outcome. Consumers who
have previously had their privacy invaded may not believe that sharing information
online will lead to the expected outcome of useful personalization. This decreased
likelihood of the expected outcome may result in a decreased motivation to partake in the
action of online profiling.
The role of past experience has been previously analyzed offline in two different
formats, with mixed results. In one instance, consumers were asked if they had
“experienced a previous invasion of privacy” (Culnan, 1993). In a second instance,
consumers were asked if they had previously dealt with the firm. In the former, prior
privacy invasion experience was not shown to have a clear association with attitudes
toward secondary information use offline, however previous privacy invasion
experiences could affect an individual’s concern for privacy (Culnan, 1993). In the latter,
14
prior firm experience was the distinguishing factor from those willing to store
information in a customer profile and those who were unwilling to do so offline (Culnan
and Armstrong, 1999). In our research, we look at the former, prior privacy invasion
experience, where previous results have not been conclusive. Previous research did not
find a significant association between previous privacy invasion experience and attitude
toward secondary information use (Culnan and Armstrong, 1999). However, we are
attempting to assess the effect of previous privacy invasion experience in a different
context, the online context, rather than the direct mail context. Therefore, we expect
previous online privacy invasion experience to have a significant effect on willingness to
partake in online personalization. Thus, we hypothesize that previous privacy invasions
decrease consumer willingness to be profiled online.
Hypothesis 4: Consumers who have previously experienced an online privacy invasion
are less willing to partake in online profiling.
Personalized Service and Personalized Advertising
Previous research has shown that firms can improve the perceived value of
services offered by mitigating a customer’s perceived risk (Heskett et al. 1990). As
discussed earlier, expectancy theory stipulates that the perceived benefit and the
perceived risk of an outcome effect consumer Valence perceptions, or the attractiveness
of the outcome to the individual. The perceived benefit of an outcome, such as useful
personalization, can motivate consumers to partake in online profiling, despite privacy
concerns. On the other hand, the perceived risk associated with an outcome can decrease
the force of action toward partaking in online personalization. In this study, we examine
two separate contexts, personalized advertising and personalized service. We expect
15
consumers to place different values on the two outcome contexts, due to varying levels of
perceived benefit and risk of the activities. Such a difference in each outcome’s
attractiveness should therefore affect consumer willingness to share information.
. Previous research has attempted to understand consumers’ “willingness to
tradeoff” personal information for benefits (Westin 1991) across various industries. Such
research found that public attitudes toward such tradeoffs were inconsistent across
various industry contexts; such inconsistencies suggest a variance in consumer perceived
benefits across industries. Industry differences are likely embedded in the service type
offered to the customer. Therefore, in this study we examine consumer willingness to be
profiled online across two types of service offerings, rather than industries. Due to the
negative connotation of advertising (McLaughlin 2002), consumers assess the benefits of
personalized service and personalized advertising to be different. However, since most of
the independent variables are such the greater concern should translate in to less
willingness to share; we expect the hypotheses to hold across the two contexts of
advertising and service. The hypothesis we expect to be different across contexts is
Hypothesis 4, dealing with previous privacy invasions. Recall that we believe that
previous privacy invasions affect Instrumentality, or the expectation that other outcomes
will lead to the expected outcome. Because the outcome of personalized advertising is
generally viewed as less valuable than personalized service, we expect that a previous
privacy invasion will have a significant effect on consumers motivation to participate in
an activity with negative connotations, namely online profiling in the advertising context.
On the other hand, we believe that personalized service will be viewed as an outcome less
16
associated with previous negative outcomes, and thus prior privacy invasions will not
have a significant effect. This leads us to the following hypotheses:
Hypothesis 4a: Consumers who have previously experienced an online privacy invasion
are less willing to partake in online profiling in the advertising context.
Hypothesis 4b: Consumers who have previously experienced an online privacy invasion
will be just as likely to partake in online profiling in the personalized service context as
those consumers who have not previously experienced an online privacy invasion.
Importance informationknowledge features
Importance of
Privacy Policies
Previous Online
Privacy Invasion
Privacy Concern
-
Willing to be
profiled online for
Personalized Service
-
Willing to be profiled
online for Personalized
Advertising
Figure 2: Research Model
4) Data and Measurement
The context for this research is the use of personal information gathered through web
sites and user willingness to allow personal information to be collected and used by firms
online. The study is based on a fresh analysis of data from a survey done at a large
Internet service provider during the summer and fall of 1998. The survey focuses on
17
issues of personal information collection through specific online scenarios, as well as
general attitudes and user demographics. The survey was designed to focuses on how
people respond to situations where personal information is collected online. In a prestudy, it was found that variance across participants in information sharing habits was
best revealed through questions based on specific online scenarios. Thus, specific
purchasing scenarios, focused around information goods and financial services, were
utilized. The survey also aimed to determine participants' general attitudes and
demographics. Attitude and demographic questions were taken from other studies, such
as Westin (1998), so the sample could be matched up against the previous studies. For
all the constructs of this study, explicit questions were used as a mechanism for deriving
information from the participants.
The survey was developed and pre-tested on non-technical employees and
summer students of the service provider, as well as with two classes at Harvard and the
Massachusetts Institute of Technology (MIT). Prospective survey participants were
selected from the Digital Research, Inc. (DRI) Family Panel. The DRI Family Panel is a
group of random Internet users that participate in product evaluations and survey
responses for Family PC magazine. Approximately one-third of the panel members are
FamilyPC subscribers, and most of the panel members who are not subscribers joined the
panel after visiting the FamilyPC Web site. Invitations to complete the Web-based
survey were emailed to 1,500 Family Panel members (selected randomly), resulting in
523 completed surveys in November of 1998, a response rate of 35%. Code numbers
were used to ensure that each respondent completed the survey only once, and a
sweepstakes was offered to encourage participation.
18
Similar to recent work on information privacy (Stewart and Segars 2002, Harris
and Westin 1998), the sample differed from a nationally representative sample in
education, Internet usage, and household income. Summary demographic information is
shown in Tables 2A and 2B.
Variable
Use Computer at Home
Table 2A: Demographics (Yes/No)
Yes
No
375 (98.4%)
2 (.5%)
Use Computer at Work
Send or Receive Email
260 (68.2%)
379 (99.5%)
119 (31.2 %)
2 (.5%)
Visit Web sites
379 (99.5%)
2 (.5%)
Are you Male or Female
Male: 183 (48.0%)
Female: 195 (51.2%)
Table 2B: Demographics
Variable
How often do you use the Internet?
(1= once a month, 7 = several times a day)
In What Year did you first hear about the Internet?
What is the highest level of school completed?
(1= Less than High School, 5= Post graduate)
Total 1997 Household Income
(1= $15k or less, 6 = $75k or over)
How many people live in your household
How many children, ages 8-12, live in your household?
Mean
6.44
Std. Dev.
0.86
1994
3.58
3.10
0.96
4.33
1.37
3.31
1.31
0.50
0.99
All of the items selected for use in this study were chosen from the larger
questionnaire administered by this large Internet service provider. Most of the items used
were single questionnaire items that aligned with the construct being assessed (For
example a single question of whether a person would be willing to participate in online
profiling for online personalized service was used to measure a consumer’s willingness to
be profiled for personalized service.) The item selection was based on the attitude the
construct was attempting to assess. There were two constructs where multiple items were
selected, namely Privacy Concern and Information Features. For these constructs, all
items that theoretically fell into either the category of consumer privacy concern or
19
features a firm could implement towards increase consumer information control, were
factor analyzed, as explained below.
Dependent Variable
Two dependent variables were used to measure consumer willingness to be
profiled for customized service and customized advertising. These five-point Likertscaled variables are: 1) consumer willingness to be profiled by a familiar site for
customized service (CSERV); and 2) consumer willingness to be profiled by a familiar
site for customized advertising (CADV). All instrument question details can be found in
Appendix A.
Independent Variables
Previous research has found that, in the context of systems development, a client’s
knowledge of a project is instrumental in allowing the client to feel in control (Kirsch
1996). In this study, we aim to extend these findings and the expectancy theory of
motivation to the online client-firm relationship; thus, we expect that client concern over
knowledge of information and use (INFO_KNOWUSE) will decrease their willingness to
share information online. The first independent variable, “knowledge of information and
use” (INFO_KNOWUSE), was measured by four three-point Likert-scaled items. The
four items were factor analyzed using a varimax rotation. All four items loaded
unambiguously on a single factor and were combined to form the “knowledge of
information and use” (INFO_KNOWUSE) construct (Cronbach alpha=0.75).
The second independent variable, “importance of privacy policy” (PRIV-POL), is
aimed at providing a contrast to the “knowledge of information and use” independent
20
variable. It is possible that consumers have no interest in knowing the details of what
information is being stored and how it is used. Rather, they may only be interested in
knowing that the company has a privacy policy. Thus, we control for the importance of
such a privacy policy through the use of a single three-point Likert-scaled item.
Prior research has also established that demographic variables are associated with
an individual’s privacy concern. For example, Culnan (1995) found that demographics,
experience with direct marketing, and privacy concern were significantly associated with
individual knowledge regarding information removal procedures. However, prior
research also suggests that such demographic differences are captured by both attitudinal
and behavioral variables (Azjen and Fishbein 1980). For this reason, we control for the
demographic variables privacy concern and previous privacy invasion1 only. An
individual’s previous experience can shape their concern in information sharing. As
shown in previous work (Culnan 1995), concern for privacy was measured using one
demographic variable, namely, whether a participant believed his or her privacy had been
previously invaded (PREV-INV). Seventy-three respondents (19.2 percent) reported
being victimized by what seemed to be an invasion of their privacy online (compared
with 21 percent from Culnan’s 1995 study and 23 percent from the 1991 Equifax survey).
Concern for information privacy is a tested, multidimensional construct (Stewart
and Segars 2002, Smith et. al 1996). However, due to the limitations of the secondary
data used, we elected instead to control for general privacy concerns, as previously done
by Culnan (1993). Privacy concern was measured by two four-point Likert-scaled items.
The two variables were factor analyzed using a varimax rotation. Both items loaded
1
We did run the model controlling for gender, education, and computer usage. The results of the variables
of interest were unchanged, and the variables did not add much explanatory power to the models.
21
unambiguously on single factor and were combined to form a “general privacy concern”
(PRIV-CONC) scale (r=0.59, p<0.0000; Cronbach alpha= 0.87)
Table 3 contains descriptive statistics for, and correlations between, the dependent
and independent variables.
Table 3
Descriptive Statistics and Inter-Construct Correlations for Dependent and
Scaled Independent Variables
Variable
Mean
S.D.
CSERV
2.12
0.79
CADV
INFOKNOWUSE
PRIV-POL
PREV-INV
PRIV-CONC
2.53
1.47
1.58
1.81
1.76
Table 4
CSERV
CADV
1.02
0.45
0.61
-0.12
-0.17
0.63
0.39
0.69
-0.01
-0.08
-0.21
-0.08
-0.13
-0.21
INFOKNOWUSE
PRIVPOL
PREVINV
0.44
0.08
0.28
0.02
0.29
0.11
Descriptive Statistics and Inter-Item Correlations for Dependent and Scaled
Independent Variables
Variable
PRIVCONC
2
PRIV-CONC1
PRIVCONC
1
1.0000
PRIV-CONC2
INFO-USE1
INFO-USE2
INFO-USE3
INFO-USE4
PREV-INV
PRIV-POL
0.4671
0.4324
0.4732
0.2263
0.1755
0.0736
0.2368
1.0000
0.3985
0.4224
0.2722
0.2453
0.1046
0.2462
INFOUSE1
INFOUSE2
INFOUSE3
INFOUSE4
PREVINV
PRIVPOL
1.0000
0.5629
0.0521
0.0678
0.0610
0.2762
1.0000
0.2450
0.2190
0.0257
0.2707
1.0000
0.7823
0.1094
0.2596
1.0000
0.1007
0.2900
1.0000
0.0253
1.0000
Methodology
Our dependent variables, willingness to be profiled for customized service
(CSERV), and willingness to be profiled for customized advertising (CADV), are both
five-point Likert scaled items. Essentially, the dependent variables are rank-ordered
ordinal variables; though level two is higher than level one, the difference in willingness
to be profiled between levels two and one is not necessarily the same as the difference
22
between level three and two. Hence, use of simple multiple regression in such a case
would lead to inefficient ordinary least squares estimates (OLS) (Kmenta 1986). In order
to overcome the limitations of OLS in this setting, a multinomial logit or probit choice
model can be utilized. Since there is an inherent ordering in the five levels of profilewillingness, we utilize an ordered probit model for estimation (Zavonia and McElvey
1975). We develop the ordered probit formulation below.
We define a latent (unobserved) continuous variable that measures the continuous
(cardinal) value of profile-willingness. In the standard regression framework, the latent
variable is a linear combination of the explanatory variables as provided below:
Latent variable= β ' X i + ε i
(1)
Note that Xi is a vector of explanatory variables for the ith consumer, β ' is the associated
vector of parameters, and ε i is the stochastic error term, which is assumed to be normally
distributed across observations in a probit model. In addition, δ i is the threshold for the
ith level of willingness to share information. The translation of the latent variable to the
observed variable is given in Appendix B. To ensure that the probabilities sum to one,
the probability of willingness to be profiled at level 5 is the complement of the sum of the
probabilities of the remaining four values. In order to ensure that all probabilities are
positive, the following conditions must be met:
δ 3 > δ 2 > δ1 > 0
(2)
Where the cutoffs δ 3 , δ 2 , and δ 1 must be estimated along with the parameter β ' .
Details on the cutoffs can be found in Appendix B. The likelihood expression for an
observation i, is stated as:
23
1
Li =
2π
 l j − zi2 
 ∫ e 2 dzi 
∏

j =1  l j −1

5
Dj
(3)
where the upper limits of the integral are: l0= - ∞ , l1= − β ' X i , l2=δ 1 − β ' X i , l3=δ 2 − β ' X i ,
l4=δ 3 − β ' X i , and l5= + ∞ . In equation (3), only one of the five integrals applies for a
specific consumer response of willingness to be profiled for customized offerings. Thus,
with each observation treated as independent, the likelihood expression for all consumers
is the product of the likelihood for each individual consumer.
n
L = ∏ Li .
(4)
i =1
The vector of explanatory variables X consists of four factors: 1) knowledge of
information and use (INFO_KNOWUSE); 2) importance of privacy policy (PRIV_POL);
3) previous privacy invasion (PREV_INV); and 4) general privacy concern
(PRIV_CONC). In addition, we include an intercept term; each element of the β vector
measures the marginal impact of the associated explanatory factor. Note that due to the
non-linearity, we cannot directly compare the parameters by magnitude. The model
specification is as follows:
β ' X i = α + β1 INFO _ KNOWUSEi + β 2 PRIV _ POLi + β 3 PREV _ INVi + β 4 PRIV_CONCi
Although we model the underlying measures of willingness to have personal
information used by a familiar site for customized service (CSERV_FAM) or customized
advertising (CADV_FAM) as a linear combination of the factors, the probability
associated with each rating is inherently nonlinear as indicated in equation (3).
24
5) Results and Analysis
We estimated the probit model for the two independent variables, customized
service (CSERV) and customized advertising (CADV), independently. The maximum
likelihood estimates of the parameters of these models are presented in Table 4.
Table 4: Maximum Likelihood Ordered Probit Estimators
Variable
INFO_KNOWUSE
PRIV_POL
PREV_INV
PRIV_CONC
Log Likelihood Value
Prob. > χ
2
CSERV
(p-value)
-0.2861**
(0.040)
0.1622
(0.102)
-0.0799
( 0.583)
-0.3364**
(0.000)
CADV
(p-value)
-0.2900**
(0.031)
0.04595
(0.641)
-0.2513*
(0.080)
-0.2996**
(0.001)
-441.9066
0.0002
-536.1750
0.0000
As predicted in our hypotheses, the main independent variable of interest, concern
over knowledge of information and use (INFO_KNOWUSE), is negatively significantly
associated with willingness to be profiled online for both models, customized service, and
customized advertising. In addition, the demographic control variable of general concern
for privacy online (PRIV-CONC) is negative and significant across both models.
However, the demographic variable of previous privacy invasion (PREV_INV) is not
significant in the case of CSERV; it is, however, positive and significant in the case of
CADV. The overall results for the ordered probit model for CSERV were also
significant (Log Likelihood= -441.90658, Prob > χ 2 =0.0002). All the significant
parameter estimates are negative. For negative parameters, a larger negative magnitude
suggests a lower probability of willingness to be profiled for customized offerings.
25
However, the relationship is intrinsically nonlinear; we cannot proportionately compare
across the negative parameter estimates.
The third column of Table 4 summarizes the results for the second model, where
the dependent variable is willingness to be profiled online for customized advertising
(CADV). Here, demographic control variables, previous privacy invasion (PREV_INV),
and general concern for online privacy (PRIV-CONC) are significant. By contrast,
previous privacy invasion (PREV_INV) was not significant in the CSERV model. The
overall results for the ordered probit model for CADV signify a robust model; the model
explains a significant amount of variance (Log Likelihood= -536.1750, Prob > χ 2
=0.0000). All the significant parameter estimates are negative in the case of model 2, the
CADV model.
These results provide support for the main hypothesis of the study, namely, that
the value of perceived control, assessed through concern over information-knowledge
features, would be negatively associated with consumer willingness to be profiled online.
Consumers that are concerned about having access to their information within company
databases are less willing to share information. The same finding holds true for
consumers who belabor the length of time in which personal information remains in a
firm database. These results suggest that managers may need to focus on such concerns
to increase the likelihood of customer willingness to be profiled by their firm. By
addressing these concerns, firms are likely to increase the number of consumers willing
to be profiled online for customized service or customized advertising.
The concern for a privacy policy, on the other hand, was neither significant in the
case of customized service, nor in the case of customized advertising. Thus, consumer
26
rated importance of a privacy policy was not found to be associated with a willingness to
be profiled online for customized service or advertising.
Lastly, we hypothesized that demographic variables, such as general privacy
concern and previous privacy invasions, would be associated with a consumer’s
willingness to be profiled for customized offerings. In both ordered probit models, the
general privacy concern is significant, providing support for the hypothesis that general
privacy concern would be negatively associated with such profile-willingness across
contexts. Hence, users more concerned with privacy are statistically less likely to share
information online. However, prior privacy invasion experience was significant only in
the context of online advertising, not in the case of online service. Therefore, users with
previous privacy invasion experience have a lower probability to be willing to be profiled
online for customized advertising. However, such a result does not hold true with regard
to online service. As noted earlier in association with expectancy theory, this may
indicate a difference in Valence perceptions, or the attractiveness of the outcome to the
individual, across the two contexts. Assuming consumers perceive the benefits of online
service to be greater than online advertising, they will be more motivated to share
information for online service. This increased motivation to share information will make
previous privacy invasion experience insignificant in the online service case, but
significant in the online advertising case, as our results suggest.
6) Discussion
Effective use of customer information is a critical success factor for firms online.
Through profiling consumers and presenting customized service and advertising, firms
27
can increase consumer value as well as firm revenue. The challenge for firms, then,
becomes collecting and using information in such a way that consumer privacy concerns
are addressed. This study examined the role of perceived control, implemented through
information-knowledge features. Such features can be used to address a consumer’s
willingness to be profiled online for customized offerings. The results have significant
managerial implications, as they suggest that firms can garner increased customer
participation in customized offerings through the implementation of informationknowledge features. Such increased consumer participation may then translate into a
competitive advantage for companies.
The findings of this study are in accordance with Expectancy theory, and also
with previous findings that indicate that perceived control over events and uses of
information, result in a consumer’s decreased concern for privacy invasion (Laufer at al.
1976). In this case, the importance of perceived control reflects an increased consumerconcern about sharing their information with online firms. While previous findings have
also shown that users with a greater privacy concern perceive little control over the use of
personal information (Stone et. al 1983), ours is the first study to show that perceived
control would increase a consumer’s tendency to be profiled online for customized
offerings.
At the outset, we expected that the importance of a privacy policy would be
significantly associated with a willingness to be profiled online. The lack of such a
significant association was initially surprising. However, upon reflection, one possible
reason for such a result became clear: privacy policies largely go unread by consumers.
In fact, according to Forrester Research, less than 1 percent of the visitors to six major
28
online travel sites during April 2001 actually read privacy policies (Regan 2001). Thus,
while consumers may rate a privacy policy as important, few of them actually take note
of the policy when using a site.
Examining the willingness to be profiled online in the specific contexts of online
service and online advertising highlights environmental differences. The most distinct
difference between online service and online advertising is the effect of importance of
previous privacy invasion. Previous work did not reveal a clear association between
previous privacy invasion experience and attitudes toward secondary information use
(Culnan 1993); therefore, this significant result in the case of online advertising is quite
interesting. The differing result across contexts regarding previous privacy invasion
suggests that people consider customized service more beneficial than customized
advertising and are therefore more willing to be profiled online, despite previous online
privacy invasions. Online advertising, on the other hand, is largely perceived as less
beneficial (McLaughlin 2002). Previous privacy invasion is, therefore, associated with a
lower willingness to be profiled online in the case of customized advertising.
Limitations and Future Research
Like other empirical research, this research also has several limitations, and the
results need to be read with caution. As described above, the study was based on
secondary data analysis of a survey designed to measure opinions toward privacy and
information sharing online. Concern for information privacy is a tested,
multidimensional construct (Stewart and Segars 2002, Smith et. al 1996). However, due
to the limitations of the secondary data, we instead controlled for general privacy
29
concerns, as previously done by Culnan (1993). Individual questionnaire items were
designed to be unbiased. However, several items, such as previous privacy invasion,
were measured using single questionnaire items. In addition, the sample has a slight bias
in favor of more educated, affluent, and Internet savvy individuals. Therefore, the results
should be viewed with some caution. The strength of the research is that the data sample
is consistent with other recent work regarding information privacy (e.g. Stewart and
Segars, 2002), and the results are consistent with theory.
Conclusion
Personalized service is increasingly becoming a source of value for both
consumers and firms (Farag and Krishnan, 2002). However, investments in
personalization may come at the cost of consumer privacy. Privacy has, therefore,
become an issue of strategic importance for companies operating in the informationcentric, networked global economy. In order to provide consumer-driven customized
service, firms must compel consumers to provide them information. Through the use of
information-knowledge features, we examined the effect of perceived control on a
consumer’s willingness to be profiled online for customized service and advertising. We
found that concern over knowledge of information and usage is associated with a
decreased consumer willingness to be profiled online across contexts. However, we also
found that the perceived benefit of customization affects the importance of previous
privacy invasion on that very willingness. In the case of customized service, where
benefit is more apparent to consumers, previous privacy invasions are not significant; the
potential benefit of the service outweighs the potential risk of a privacy invasion. In the
30
case of customized advertising, on the other hand, the benefit is less apparent, and the
risk of an intrusion, (i.e. email spam) is more apparent. In such a case, previous privacy
invasion is significant. Thus, companies must focus on reducing such perceived risk
through implementing information-knowledge features.
For managers to be successful in encouraging consumers to partake in online
customized service, their firm’s entire information practices should be given unfettered
accessibility. Such accessibility may even be provided in a limited format. Future
research may therefore examine, over time, the effectiveness of varying levels of
accessibility on consumer willingness to be profiled online. In this study, we have
provided results managers can utilize to encourage consumer participation in online
profiling for customized service and advertising. Our findings should therefore help
firms tailor their online service offerings and maximize information collection across
various consumer segments, as a result.
31
Appendix A: Survey Instrument Details
Dependent Variables
Willingness to have personal information used by a familiar site for customized
service (CSERV) was measured by a five-point Likert-scaled item ranging from
“Definitely Not” to “Definitely Would”:
•
Some Web sites assign visitors special user identification numbers. Web
browsers can send these numbers back to the site automatically on a return
visit. This allows Web sites to recognize return visitors and provide
customized service based on previous activities. If a site that you frequented
asked you whether it could assign you an identification number so that it
could provide you with customized service, would you agree? (mean=2.12,
s.d.=0.79).
Willingness to have personal information used for customized advertising by a
site the user was familiar with (CADV) was also measured by a single five-point Likertscaled item ranging from “Definitely Not” to “Definitely Would”:
•
Some Web sites use special identification numbers not only to customize site
content, but also to customize advertising that appears on the site and make
sure that visitors are not repeatedly shown the same advertisements. If a site
that you frequented asked you whether it could assign you an identification
number so that it could provide you with customized advertising, would you
agree? (mean=2.53, s. d. =1.02)
These questionnaire items were given in the order presented.
32
Independent Variables
The first independent variable, “knowledge of information and use”
(INFO_KNOW), was measured by four three-point Likert-scaled items ranging from
“Very important” to “Not important”:
•
Importance of whether a company will allow me to find out what
information about me they keep in their databases (mean=1.40, s.d.=0.58)
•
Importance of whether a site tells me how long they will retain
information they collect from me (mean=1.87, s.d.=0.72)
•
Importance of the purpose for which the site wants to collect info from me
(mean=1.29, s.d.=0.52)
•
Importance of whether a site is going to use the information they collect
from me in a way that will identify me (mean=1.30, s.d.=0.54)
The second independent variable, importance of a privacy policy (PRIV_POL),
was assessed through the use of a single three-point Likert-scaled items ranging from
“Very important” to “Not important”:
•
Importance of whether or not the site posts a privacy policy (mean=1.58,
s.d.=0.63)
The third independent variable, whether a participant believed his or her privacy
had been previously invaded (PREV-INV), was assessed with a single yes/no item. The
item used was:
•
Have you ever personally been the victim of what you felt was an invasion
of your privacy when using the Internet? (mean= 1.81, s.d. = 0.3896)
33
The fourth independent variable, privacy concern (PRIV-CONC), was measured
by two four-point Likert-scaled items, ranging from “Very concerned” to “Not concerned
at all”:
•
How concerned are you about threats to your personal privacy in America
today? (mean= 1.77, s.d.= 0 .75 )
•
How concerned are you about threats to your personal privacy when using
the Internet? (mean= 1.75, s.d.= 0 .71 )
Appendix B: Mapping of the Latent Variables to the Observed Levels
Underlying the indexing in ordered models is a latent but continuous descriptor of
the response. In an ordered probit model, the random error associated with the
continuous descriptor is assumed to follow a normal distribution. The unobserved
willingness to share score is mapped to the observed willingness to share levels as shown
below, with ε i distributed as a standard normal. The probability of each value of the
score is then beside it.
Level 1 if β ' X i + ε i ≤ 0
Prob[Level 1] = Φ (− β ' X i )
Level 2 if 0 ≤ β ' X i + ε i ≤ δ 1 ,
Prob[Level 2] = Φ (δ 1 − β ' X i ) − Φ (− β ' X i ),
Level 3 if δ 1 ≤ β ' X i + ε i ≤ δ 2 ,
Prob[Level 3] = Φ (δ 2 − β ' X i ) − Φ (δ 1 − β ' X i ),
Level 4 if δ 2 ≤ β ' X i + ε i ≤ δ 3 ,
Prob[Level 4] = Φ (δ 3 − β ' X i ) − Φ (δ 2 − β ' X i ),
Level 5 if δ 3 ≤ β ' X i + ε i .
Prob[Level 5] = 1 − Φ (δ 3 − β ' X i ),
Appendix C: Random Parameters Ordered-probit Model
34
In this model, we allow the effect of the parameters to vary across consumers
according to a known multivariate distribution. Generically, we can rewrite the latent
variable equation (1) as:
Latent variable= Γi' X i + ε i
(B1)
where Γi = β + γ i . The vector β represents the mean effect of the parameters across all
customers, whereas γ i represents the vector of random deviations from the mean vector
β for customer i. Therefore, for the sake of parsimony, we allow for variation of the
parameters across individual customers. Since γ i follows a multivariate distribution, Γi
also follows a multivariate distribution. Since we include the mean vector in Equation
(B1), the mean vector of γ i other than a zero vector is not identified.
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