Download Evaluating Personalization and Customization from an Ethical Point

Proceedings of the 37th Hawaii International Conference on System Sciences - 2004
Evaluating Personalization and Customization from an Ethical Point of View:
An Empirical Study
Horst Treiblmaier, Maria Madlberger, Nicolas Knotzer, Irene Pollach
Vienna University of Economics and Business Administration Austria
{horst.treiblmaier, maria.madlberger, nicolas.knotzer, irene.pollach}@wu-wien.ac.at
suggesting that customization is
questionable than personalization.
Abstract
This paper examines whether classic ethical
theories can solve the ethical dilemmas associated with
user-controlled customization and system-driven
personalization of Web sites. Based on the notion that
data sensitivity is not a universal concept but comes in
different levels of intensity, we conducted an Internetbased survey among consumers to determine their level
of data sensitivity and their attitudes towards
personalization and customization. Our results have
shown that users can be classified into different groups
who differ significantly in terms of data sensitivity.
Applying ethical theories to personalization and
customization has led to conflicting conclusions, but
they are in line with the findings from the survey,
ethically
less
1. Introduction
In this paper we are investigating whether classic
ethical theories can solve the ethical dilemmas
associated with the customization and personalization
of Web sites in respect of user privacy. The terms
customization and personalization are often used
interchangeably in both academic and non-academic
literature. The resulting ambiguities arise from
different views on personalization and customization.
Table 1 gives an overview of different notions of
personalization and customization found in academic
literature.
Table 1: Personalization and customization in academic literature
Source
Fink et al., 2002 [1]
Used term(s)
Personalization
Chiasson et al., 2002 [2]
Personalization
Kalyanam and McIntyre,
2002 [3]
Billsus et al., 2002 [4]
Ardissono et al., 2002 [5]
Ansari and Mela, 2003 [6]
Personalization
Sundbo, 2002 [7]
Lampel and Mintzberg,
1996 [8]
Nielsen, 1998 [9]
Personalization
Personalization
Customization
Customization
Personalization,
Customization
Context
Marketing,
Communication
Meaning/Application
One-to-one relationships with customers; direct access to
personally relevant news, seamlessly integrating user
preferences into the existing infrastructure, collecting
information about user interests
Information Systems Personalization of information in order to customize
(IS), Humaninteractions with end-users and reduce interaction
Computer-Interaction complexity
(HCI)
Marketing
One of the instruments of e-marketing mix, aspect of
segmentation
IS
Personalization as a result of adaptive technologies
IS
One-to-one recommendation of products
Marketing
Customization of communications by means of clickstream
data
Services
Customization of services as the opposite of service
standardization
IS, HCI
Customization: under direct user control; the user explicitly
selects between certain options
Personalization: driven by computers which try to serve
individualized pages to users based on some kind of model
of their individual needs
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Source
Zahay and Griffin, 2003
[10]
Used term(s)
Customization,
Personalization
Context
Marketing, B2B
sector
Coener, 2003 [11]
Customization,
Personalization
Services
Hirsh, Basu and Davison,
2000 [12]
Customization,
Personalization
IS
In this paper the information systems (IS)
perspective towards personalization and customization
seems to be the most appropriate since we are
investigating personalized Web sites. In the context of
information systems both terms can be defined as
modifications of the functionality, interface,
information content or distinctiveness of an
information system with a view to increasing the
personal relevance to an individual user [13]. The
difference between personalization and customization
lies in the control of the adaptation process [9] [14].
Customization is a user-initiated and user-driven
process. It uses adaptable system-components which
users can tailor to their specific needs. Adaptable
systems use static profiles, which may be changed by
the user, like e.g. the Web Portal DailyRoutine
(www.dailyroutine.com), which enables users to adapt
the content and layout to their preferences.
By contrast, personalization is system-initiated,
system-driven and requires adaptive components. In
order to make modifications appropriate to the needs of
the individual, both approaches require detailed
information about the user. Personalization, however,
additionally requires the system to monitor user
behavior in order to adapt automatically and users are
thus unable to control how the system adjusts to their
behavior. Personalized systems employ user profiles,
which are changed dynamically by the system.
Amazon (http://www.amazon.com) monitors buying
behavior and click stream data of customers in order to
suggest products which may be of interest to the
customer.
From an ethical perspective, the distinction between
personalization and customization is of major
importance, as both personalization and customization
raise privacy concerns, yet to varying extents.
Personalization raises more serious ethical concerns
than mere customization, as the latter requires users to
explicitly
control
the
adaptation
process.
Personalization, by contrast, tracks user behavior on
Web sites, which conflicts stronger with the users' right
Meaning/Application
Customization activity: Use information from value-added
chain to create product for individual customers;
Personalization capability: respond to customers by taking
into account their individual responses to communication
Customization: Web site users can actively dictate the
information on the site, match of categorized content to
profiled users
Personalization: more passive role, content is filtered for
users
Personalization as “self-customizing” software, systems are
automatically customized to the personal characteristics of
the user
to data privacy and security [15]. Although online
vendors which track user activity might exploit this
information for the sole purpose of increasing the
system's convenience for users without being aware of
the ethical dimension of this practice, they may as well
misuse the data to harass users with personalized
advertising material or pass on this information to third
parties [16].
The ethical implications of personalization have
already been the focus of academic study (cf. [17] [18]
[19]). Sheehan and Hoy surveyed a consumer sample
as to their attitudes towards online privacy concerns
and found that consumers consider unsolicited e-mails
from companies a minor privacy issue if they have
done business with this company before and are more
willing to divulge personal information if they are
provided with something in return. However, their
study averages all findings and fails to acknowledge
that data sensitivity is not a universal concept but
comes in different levels of intensity [20]. Culnan
found that consumers differ in their attitudes towards
the secondary use of personal information in direct
marketing and thus perceive this invasion of privacy
with different levels of intensity [21]. This paper
explores whether Internet users can be classified into
different groups according to their data sensitivity on
the Web. It also examines whether classic ethical
theories can help to solve the ethical dilemmas
associated with personalization and customization,
based on the hypothesis that ethical dilemmas might
not be experienced with the same intensity by all users
due to their potentially differing levels of data
sensitivity.
2. Background
Normative ethical theories seek to arrive at
conclusions about whether an action is morally good or
bad rather than merely describe the ethical dilemma
associated with an action [22]. However, the moral
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intensity of the potential privacy invasion varies widely
across normative ethical theories, which may lead to
divergent views of the ethicality of customization and
personalization. This section seeks to outline the main
foci of the most important normative ethical theories
and juxtaposes them to relevant aspects of stakeholder
theory, all of which may be helpful in determining
whether personalization and customization are
ethically problematic.
2.1. Normative ethical theories
Normative ethical theories can be classified into (1)
deontological theories, which evaluate the act as such,
(2) teleological theories, which take into account the
consequences of an act, and (3) virtue ethics, which
concentrates on the agent's character [23].
Deontologism focuses on the rightness or
wrongness of an act, irrespective of whether the
consequences of the act are morally good or bad [24].
Deontological theories are based on rights and duties
and have universal maxims [25]. An example of a
deontological theory is Kant's categorical imperative,
according to which people should always be treated
with dignity, and never be used as mere instruments to
achieve one's own goals [26]. The two major criteria
incorporated in the categorical imperative are
universality and reversibility. Thus, an action is
considered morally right if the agent would also want
all other people to act the same way in a similar
situation [22]. The downside of this approach is that it
focuses solely on duties and the intrinsic values of
actions, while it completely ignores the consequences
of actions for others [26]. When applying
deontologism to issues of customization and
personalization only the process is analyzed while the
results — which may or may not benefit the user —
are ignored.
In his classic paper "Four Ethical Issues of the
Information Age", Richard Mason developed the
PAPA (Privacy, Accuracy, Property, and Accessibility)
model, which is rooted in the deontological tradition.
Similar to Kant, Mason postulates the universal maxim
that information technology and the information it
handles must be used to enhance the dignity of
mankind. Privacy, accuracy, property, and accessibility
are the key factors to consider when designing
information systems. Mason further holds that the main
questions concerning privacy are to what extent a
person must reveal personal information to others,
under what circumstances, and how preventive
measures can be taken to guarantee privacy. Further
problems in connection with accuracy are how to
ensure authenticity, fidelity and accuracy of
information, and how to determine who is responsible
for information accuracy. Concerning property it has to
be clarified who owns the information and what prices
are fair for the information divulged. In this context it
is also of central interest who should own the channels
for transmitting information. The last relevant field is
accessibility. The fundamental questions are who
should have access to information and under what
conditions [27]. These four issues of the "Information
Age" are of high relevance especially for the design of
personalized and customized information systems
where a lot of confidential information is stored,
processed and used to create personal profiles.
Teleological theories, by contrast, focus primarily
on the consequences, results, goals and purposes of
actions [28]. Social contract theory is one such
teleological normative theory, stating that every
exchange should be based on reciprocity and equality
[29].
Utilitarianism,
also
referred
to
as
consequentialism, is another teleological theory,
evaluating the morality of an action in terms of costs
and benefits to society [23]. If the outcome of an action
is a surplus of benefits over costs, utilitarianism
endorses the goodness of this action [28]. The
downside of utilitarianism is that it seeks to produce
the greatest good for the greatest number, favoring the
interests of the majority, while a minority may suffer
from the consequences of the action [26]. When
teleological theories are applied to issues of
customization and personalization the overall outcome
is just one single criterion whereas the process is rather
irrelevant. This has interesting implications for the use
of personal data, especially when the expected
outcome exceeds the sum of all inputs.
Virtue ethics focuses on the traits of an ethical
subject, e.g. character, motivation or intention. It
investigates the agent-intrinsic reasons for responding
one way or another to external or internal forces [23].
Virtues are understood as "an acquired disposition that
is valued as part of the character of a morally good
human being" [22]. Examples of virtues include
honesty, unselfishness, or fairness. The famous virtue
ethicist Aristotle claimed that all virtues are at the
center of a continuum between deficiency and excess.
He stressed that the uppermost goal of all human
behavior should be to find a balance between the two
extremes of the continuum by striving for the middle
ground, which he referred to as the "golden mean"
[23]. Virtue theory argues that an action is morally
good if a morally virtuous person would exhibit this
behavior habitually. Also, actions that make a person
more honest, more caring or more responsible are
morally good, while actions that achieve the opposite
are morally bad. This theory stresses the nature of the
organization rather than its goals or processes, which
means that the usage of personal information by
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"responsible organizations" could serve as a kind of
benchmark for the whole industry.
2.2. Stakeholder theory
When examining the ethical aspects of
personalization and customization, stakeholder theory
appears to be useful as well. In this context stakeholder
theory focuses on (1) the stakeholders, (2) their
perspectives concerning privacy issues, (3) their
interests underlying these perspectives, and (4) the
values affecting their attitudes [30]. Although
stakeholder theory is not a purely ethical theory, it is
still useful in resolving ethical dilemmas and conflicts.
A careful analysis of a company's stakeholders may
help decision makers to see all ethical implications of
their business conduct and may prevent unintended
ramifications for a company's stakeholders [31]. For
privacy claims it is important that the following three
principles be taken into consideration [32]: First, the
access principle holds that stakeholders should have
access only to the information which is necessary in a
given situation. Second, the representation principle
refers to the presence of all relevant stakeholders, i.e.
the stakeholders should have the possibility to
communicate their values and interests. Ultimately, the
power principle states that all stakeholders should have
equal power to protect their interests. If one of these
principles is violated, the risk of stakeholder claims
being unacknowledged or underrepresented will rise.
Obviously, there are no situations where all three
principles are abided by, but taking these principles
into consideration is an important step towards the
understanding, identification, and analysis of privacyrelated problems. Caudill and Murphy hold that ethical
conflicts among stakeholder groups can be resolved by
requiring each group to trade off certain rights to other
groups and by ensuring that these trade-offs are dealt
with in a fair and just manner, balancing each
stakeholder group's benefits and harms [29]. This
smacks of utilitarianism, but differs substantially from
the former in that it considers individual stakeholder
groups rather than society as a whole. Due to this
individualistic and somehow pragmatic approach we
considered stakeholder theory quite suitable for
assessing ethical issues pertaining to privacy and data
sensitivity.
3. Research method
Our research concentrates on determining which
ethical theory best fits the ethical issues arising from
personalization and customization. After briefly
describing the methods used, we discuss the empirical
data and then consider the ethical implications of our
findings (Section 5) by pointing out the pros and cons
of each theory.
With personalization and customization being
increasingly used on Web sites we decided to conduct
an online survey among Internet users to determine
their attitudes towards adaptive and adaptable systems.
The geographic scope of this investigation is Austria,
where Internet users still differ demographically from
the total population. Only 50% of Austrians older than
14 years use the Internet. Strikingly, 87% of Austrians
between 14 and 19 years do so, but only 10% of the
60+ population. Overall, 62% of men but only 40% of
women use the Internet in Austria [33].
Clearly, online surveys can never achieve
representativeness unless the survey intends to study
only Internet users, which is the case in our study.
Another problem of online surveys is the self-selection
of the respondents, which may result in a bias, as
Internet users decide themselves whether or not they
fill out the questionnaire. Thus, users who are highly
involved with the topic are more likely to complete the
survey than uninvolved users. Furthermore, Internet
users who navigate the Internet intensively may be
over-represented in the sample, as they might
participate in surveys more often than others. For our
study, highly involved and intensive Internet users
were of special interest, since this survey covers the
sensitive topics of privacy and data protection.
Therefore, we conducted an Internet-based survey,
mindful of the above-mentioned limitations and
drawbacks of online studies.
First, a pretest was conducted in order to ensure the
understandability and usability of the questionnaire. 10
persons of different age groups with varying levels of
computer experience were asked to fill out the survey.
Subsequently, the questionnaire was adapted and
finally posted on the Internet in March 2003. For the
purpose of obtaining a satisfactory sample size the
questionnaire was linked to several online forums
popular in Austria. At the end of the inquiry period 250
completed forms had been collected. The questionnaire
contained a total of 42 questions, pertaining to
demographics, Internet use, data sensitivity,
personalization and customization.
Table 2 contains a demographic overview of the
sample. The sample turned out to be very
representative of Austrian Internet users in respect to
sex and profession, but was slightly biased in terms of
education and frequency of Internet use due to the
above-mentioned biases inherent in online surveys.
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Table 2: Characteristics of Respondents
Sex
Male
Female
Age
14 –19 years
20 – 29 years
30 – 39 years
40 – 49 years
50+
Education
Secondary school
Apprenticeship/
Vocational school
High school grad.
Technical College
University
Occupation
Management
Administrative/technical
Self-employed
House-wife or husband
Retired
Student
Other
14.4%
42.0%
12.4%
1.6%
2.0%
22.0%
5.6%
6.8%
22.0%
Experience on the Internet
less than 6 months
6 – 12 months
2.8%
2.0%
40.0%
8.0%
23.2%
1 – 2 years
2 – 4 years
more than 4 years
9.6%
29.2%
56.4%
52.8%
47.2%
5.2 %
44.0 %
24.8%
16.4%
9.6%
To study the sample's levels of data sensitivity, we
transformed those questions referring to data
sensitivity into 10 items with three different values
each. These items are used to measure the evaluative
dimension of data sensitivity, which is appropriate
when conducting a survey over the Internet. The items
pertain to data collection and data sharing and their
three values represent approval, indifference or
disapproval.
Frequency of Internet Use
Daily
Several times per week
Several times per month
Less than once a month
89.6%
9.2%
1.2%
0.0%
Table 3 summarizes the variables for data
sensitivity and their three possible answers. In order to
ensure reliability we used the Kuder-Richardson
formula 20 for dichotomous variables (Į = .76). An
expert rating was used to establish content validity, i.e.
whether the items represent all situations we sought to
measure. Based on this rating the following variables
were chosen:
Table 3: Indicators of Data Sensitivity
Variable
VAR20
VAR26
VAR27
VAR28
VAR30
VAR31
VAR36
VAR37
VAR38
VAR40
Description
Storage of personal data by online shops to avoid
reentering data in the future
Use of cookies by online shops
Web sites storing data of their visitors
Fears of “big brother” in the context of the Internet
Companies gathering navigational data without the
user's knowledge
Receiving newsletters or other messages from Web
sites at which the user is registered
Readiness to divulge personal data when a personal
advantage is offered and the data are treated
confidentially
Storage of personal data in order to facilitate the
finding of products and information
Positive experience with an online shop enhances
readiness to divulge personal data
Sharing of personal data with third parties so that users
receive potentially interesting advertisements
negative
Answer categories
indifferent
positive
negative
negative
justified
objectionable
indifferent
indifferent
indifferent
indifferent
positive
positive
not justified
not objectionable
annoying
indifferent
not annoying
no
indifferent
yes
negative
indifferent
positive
no
indifferent
yes
negative
indifferent
positive
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4. Empirical Investigation
We have conducted a cluster analysis in order to
identify different groups of Internet users in respect to
their levels of data sensitivity. As can be seen from
Table 3, we used three answer categories that were
considered to be equally spaced. Our analysis has
shown that these variables are multi-dimensionally
heterogeneous and so a cluster analysis can be
conducted. A single-linkage analysis has proved that
the sample does not contain any outliers. Therefore, we
have decided to apply the Ward method (squared
Euclidean distance), which uses an analysis of variance
approach to evaluate the distances between clusters.
According to the elbow criterion, four clusters (groups)
have been identified, which differ in their levels of data
sensitivity and form the basis for our further analyses.
The size of each group in absolute and relative terms is
shown in Table 4.
Table 4: Size of groups
Groups
A
B
C
D
Total
Absolute
108
68
39
35
250
Relative
43.2%
27.2%
15.6%
14.0%
100.0%
A discriminant analysis has led to three functions
that have a significant discriminatory power (WilksLambda= .036, Chi-Squared= 805.232 (df: 30), p <
.001). The centroids of these clusters are shown in
Figure 1. The three functions are made up of variables
that lead to an ideal grouping. We decided not to name
them due to their inherent variety. They could be best
described as “amount of distrust” (function 1),
“nescience of data collection and usage” (function 2)
and “facilitation of Internet surfing” (function 3).
B
3
F
u
n
c
t
i
o
n
1
2
1
D
0
A
-1
-2
C
-3
4
3
2
1
Function 2
0
-1
-1,5 -1,0
,5
-,5 0,0
1,0 1,5
Table 5 gives the common correlations within the
groups between the discriminatory variables and the
standardized canonical discriminatory functions. The
variables are ranked according to their absolute level of
correlation with the function.
Table 5: Structure matrix
VAR38
VAR37
VAR36
VAR26
VAR28
VAR31
VAR30
VAR20
VAR27
VAR40
Function 1
.685*
.358*
.350*
.248*
.141*
.259
.123
.456
.294
.177
Function 2
-.384
.047
.040
.117
.037
.806*
.147*
.055
.181
.061
Function 3
-.572
.108
-.058
.226
.042
-.373
.117
.593*
.304*
.296*
Figure 2 depicts a polarity profile, illustrating the
differences between the individual groups. We used
this univariate interpretation in order to highlight the
characteristics of the four groups. Based on this profile,
Group C can be characterized as chiefly data sensitive
and Group B as rather data insensitive, whereas the
profiles of Groups A and D are heterogeneous.
Members of Group B have a low aversion to providing
online shops with personal information, while
members of Group C are very reluctant to do so, even
if this would increase their level of convenience
(VAR20). Interestingly, Group B ranks only second in
terms of data insensitivity for VAR31 (receiving
unsolicited newsletters) and VAR38 (enhanced
readiness to divulge personal data because of previous
positive experience). It is also striking that the values
for VAR20, VAR26, and VAR27, which are questions
pertaining to data collection in general, are very similar
for Groups A, D and C, whereas those of Group B are
markedly different. Although Group B is apparently
not afraid of the collection of personal data in general,
it strongly objects to unethical practices such as the
unauthorized collection of data (VAR30) or the sharing
of their data with third parties (VAR40), similar to
Group C. However, Group B appreciates the
convenience of personalization and customization
(VAR20, VAR36, VAR37), while Group C does not. It
is also interesting to note that Group C is not even
willing to divulge personal information to online shops
if it has done business successfully with the shop
before (VAR38).
Function 3
Figure 1: Centroids of clusters
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VAR35
Variable
VAR21
VAR22
VAR23
VAR32
VAR34
Figure 2: Polarity profile of the four groups
(0 High data sensitivity, 1 Low data sensitivity)
In a next step we tested whether the groups differ
significantly regarding their attitudes towards
customization and personalization. We used 10
different indicators, which are summarized in Table 6.
The questions included in the questionnaire did not
bear direct resemblance with the variables. Rather, the
variables were hidden in examples and paraphrases to
make sure all respondents could relate to the questions
and fully understood them. The comprehensibility of
the questions was also tested in the pilot survey.
The nominal-scale answer categories call for a chisquare test for interrelations. The results reveal that all
groups differ highly significantly in their answers to
almost all questions (p < .01). Only variable 14 showed
a significance of .015.
Wish to be asked for
no
indiff.
yes
personal preferences
Attitude towards Personalization
Description
Answer categories
Attitude towards a
neg. indiff.
pos.
personalized
homepage
Previous use of
no
indiff.
yes
personalized
homepages
Quality of
inf- indiff.
suppersonalized offers
erior
erior
Wish for more
no
indiff.
yes
personalized offers
Wish for more
no
indiff.
yes
adaptive Web sites
The results for the variables pertaining to
customization have shown that Group B has the most
positive attitude towards customization among the four
groups, while Group C has the most negative attitude
towards customization on all five variables. The
substantial discrepancies between attitudes of Group C
and attitudes of Group B are evident from the chart in
Figure 3. Especially VAR33 and VAR35 clearly show
that Group C does not wish for more customized Web
sites in the future either. This suggests that user
perceptions of customization are impacted by the users'
general level of data sensitivity rather than the quality
of the customized Web sites.
Table 6: Indicators of Attitude towards
Customization and Personalization
Attitude towards Customization
Variable
VAR14
VAR15
VAR17
VAR33
Description
Attitude towards a
customized homepage
Previous use of
customized
homepages
Attitude towards
customized news
Wish for more
customizable Web
sites
Answer categories
neg. indiff.
pos.
no
indiff.
yes
neg.
indiff.
pos.
no
indiff.
yes
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14%
10%
var 34
var 35
9%
5%
32%
57%
25%
15%
17%
13%
var 32
var 33
20%
13%
47%
50%
21%
20%
26%
var 23
var 17
69%
38%
76%
15%
50%
31%
63%
3%
8%
var 22
var 15
23%
15%
38%
19%
44%
20%
var 21
40%
var 14
26%
60%
56%
0%
20%
40%
A
B
60%
C
72%
48%
0%
80%
20%
100%
D
Figure 3: Positive attitude towards customization
Figure 4 depicts the results obtained for the
variables pertaining to personalization. They are
consistent with those obtained for the customization
variables, but differ in that the discrepancies between
results for Groups C and B are considerably higher.
Thus, Group C opposes personalization even more
strongly than customization. Also, the results obtained
for VAR32 and VAR34 indicate that Group C does not
intend to use personalized sites in the future either.
Hence, similar to customization, the quality of a
personalized Web is apparently unable to convince
data-sensitive users of the convenience provided by
personalization.
49%
21%
40%
A
60%
B
C
80%
100%
D
Figure 4: Positive attitude towards personalization
5. Implications of findings
As the results above have shown, different user
groups have varying attitudes towards customization
and personalization, which can be put down to the
ethical issues associated with tracking user behavior
and adapting Web sites to users without their consent.
The ethical theories introduced in Section 2 are wellsuited for determining whether personalization and
customization are morally good or bad. From a Kantian
perspective, for example, the collection of consumer
information without the consumers' consent can never
be ethically justified, simply because the act as such
would be considered ethically wrong and not because
the online merchant may misuse the information
collected. Deontologism would not even permit the
monitoring of members of Group B, although they
would not mind being monitored. Thus, deontological
theories deem personalization of any kind as morally
wrong, while customization as a user-imitated
adaptation process is ethically acceptable because it
does not impose anything on users without their prior
consent.
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According to social contract theory, personalized
Web sites could be seen as the benefits consumers
obtain for divulging personal information to
companies, which the companies could use to make
their offerings more appealing and increase their sales.
From this perspective, personalization creates a winwin situation. However, considering that consumers
differ in terms of data sensitivity, these benefits would
have to come in different forms to fully compensate all
consumers for the data they have traded off and to
abide by the principle of equality and reciprocity. As
this is hardly feasible, social contract theory would
endorse customization as ethically good but would
disapprove of personalization, since it cannot ensure an
equal exchange.
From a Utilitarian perspective, personalization in
general is ethically acceptable if the potential benefits
for the users, e.g. higher usability or more targeted
offers, exceed the harms resulting from the potential
violation of privacy which some users may experience.
However, taking into account the four consumer
groups identified above, personalization cannot be
justified for members of Group C, for whom harms
would exceed benefits, but is absolutely justified for
Group B, and to a certain extent for A and D as well.
Customization, in turn, would meet the requirement of
benefits over costs for all four groups, as it does not
impose anything on users and hence does not cause any
harm.
According to virtue ethics, a balance is to be
achieved among company goals (better customer
relationship management) and consumer goals (data
privacy). This balance is found at the center of a moral
continuum. One extreme of the continuum would be
for companies not to personalize Web sites at all and
the other extreme would be to use all information they
can possibly get hold of, maybe even without the users'
consent, to personalize their Web sites as much as
possible. The "virtuous" solution would be some
middle ground that exhibits the virtues of honesty,
responsibility and caring, e.g. full disclosure of all data
collection practices, strict confidentiality concerning
all data obtained from consumers, and opt-in rather
than opt-out facilities. Since virtue ethics focuses
exclusively on the agent, the four consumer groups
identified above do not affect the conclusion reached
about personalization at all. Customization, in turn, is
user-driven and thus not covered by virtue ethics at all.
Hence, virtue ethics is not very helpful in determining
the ethicality of personalization and customization as
to the four consumer groups identified above.
Stakeholder theory is well-suited to provide a
starting point for resolving the ethical dilemmas
encountered in personalizing and customizing Web
sites. Its strength lies in the fact that it pays attention to
individual groups rather than society as a whole. Thus,
the four groups of consumers identified above could be
regarded as individual stakeholder groups whose
interests need to be considered when making decisions
in ethically ambiguous situations. According to
stakeholder theory, customization would not be an
ethical problem, as it does not involve any trade-offs
on the part of the consumers, as opposed to
personalization. To make for a fair distribution of
trade-offs among stakeholder groups, these consumer
groups should be offered different levels of
personalization rather than a one-size-fits-all approach
to personalization.
6. Conclusion
Our results have shown that there are different
groups of online customers who differ significantly in
terms of data sensitivity. Thus, the identification of
user Groups A to D provides new insights into the
implications ethical theories have for personalization
and customization. The ethical theories discussed
above have led to conflicting views on the ethical
problems associated with adapting Web sites to user
behavior. Also, the fact that users exhibit differing
levels of data sensitivity influences the conclusions
reached by those ethical theories focusing on aspects
other than the agent. Notably, stakeholder theory
appears to be a viable solution to resolve this ethical
issue, although it is not a purely ethical theory. Our
study supports the conclusion that customization is
ethically less questionable than personalization, as it
does not impose features on users but is entirely
controlled by users. In practical terms, this means that
companies tracking user behavior need to disclose their
data-gathering practices and should offer opt-in rather
than opt-out facilities to fulfill their duty of telling the
truth and respecting others.
Acknowledgements
The authors wish to thank Armin Gegenbauer, who
assisted in the collection of the data, and Andreas
Strebinger, who helped with the statistical analysis.
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