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Behaviour & Information Technology, 2015
Vol. 34, No. 1, 45–56, http://dx.doi.org/10.1080/0144929X.2013.805244
Compensation paradox: the influence of monetary rewards on user behaviour
Hwansoo Leea, Dongwon Lima, Hyerin Kimb, Hangjung Zoa∗and Andrew P. Ciganekc
aDepartment of Management Science, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu,
Daejeon 305-701, Republic of Korea; bDepartment of Anthropology, London School of Economics and Political Science (LSE),
Houghton Street, London WC2A 2AE, UK; cCollege of Business and Economics, University of Wisconsin-Whitewater,
800 West Main Street, Whitewater, WI 53190 1790, USA
(Received 22 July 2012; final version received 1 May 2013)
Many e-commerce companies collect users’ personal data for marketing purposes despite privacy concerns. Information-
collecting companies often offer a monetary reward to users to alleviate privacy concerns and ease the collection of personal
information. This study focused on the negative effects of monetary rewards on both information privacy concerns (IPC) and
information disclosure. A survey approach was used to collect data and 370 final responses were analysed using a two-way
analysis of variance and a binomial logistic regression model. The results show that monetary rewards increase IPC when
an information-collecting company requires sensitive information. Additional results indicate that building trust is a more
effective way of collecting personal data. This study identifies how organisations can best execute information-collection
activities and contributes additional insights for academia and practitioners.
Keywords: monetary rewards; information sensitivity; information privacy concerns; information providing intention;
information misrepresentation intention
1. Introduction
Many e-commerce companies collect consumers’ personal
information online and this is an important process to facil-
itate effective customer service and marketing (Hann et al.
2007). Data collected online can be abused by collect-
ing companies (e.g. improper access and secondary use),
which creates barriers for consumers of online services
(Kobsa et al. 2011). The U.S. Federal Trade Commis-
sion reports that the number of user complaints about
privacy has increased every year since the emergence of
Internet shopping (Antón et al. 2010). Online data col-
lection is an essential business practice despite privacy
concerns. Information-collecting companies use a variety
of approaches to alleviate privacy concerns, such as offer-
ing monetary rewards, adopting new security technologies,
and establishing online privacy policies.
Monetary rewards are a common means to elicit online
participation and have been repeatedly shown to be effective
in improving response rates (Dillman et al. 2009). Monetary
rewards include currency or currency-equivalent rewards
like gifts and coupons. Researchers have argued that mone-
tary rewards may not guarantee quality responses and result
in negative consequences like response errors. O’Neil and
Penrod (2001) believe that individuals give responses of
worse quality when offered monetary rewards.
Most information system studies have focused on the
positive effect of monetary rewards on information privacy
concerns (IPC) but have produced inconsistent results. No
clear relationship exists between monetary rewards and
IPC. Previous studies argued that the offer of monetary
rewards offsets an informant’s privacy concerns, but Taylor
et al. (2009) found an insignificant relationship between
monetary rewards and privacy concerns. Research has also
found that monetary rewards can work differently based
upon context, such as who collects the data and what data
are collected (Phelps et al. 2000, Sheehan and Hoy 2000,
Malhotra et al. 2004).
The purpose of this study is to explore the effect of
monetary rewards on IPC and related behaviours such
as information misrepresentation and disclosing. Web-
sites were developed for the experiment and a large-
scale survey approach was used to collect data analysed
using a two-way analysis of variance (ANOVA) and
a binomial logistic regression model. The results show
that monetary rewards can increase IPC when compa-
nies require sensitive information from individuals while
trust is a better way to persuade individuals to disclose
personal information. Information-collecting bodies must
understand the conditions which monetary rewards work
efficiently.
The remainder of this study is organised as follows.
Section 2 provides the theoretical background; Section 3
presents the research model and hypotheses; Section 4
describes the research method of this study; the results are
∗Corresponding author. Email: joezo@kaist.edu
© 2013 Taylor & Francis
46 H. Lee et al.
presented in Section 5; Section 6 concludes the study with
implications, limitations and directions for future research.
2. Theoretical background
2.1. Information privacy concerns
Privacy has been addressed by researchers in various
research domains including psychology, sociology and
information systems (Dinev and Hart 2004). Privacy is
‘the right to be let alone’ (Warren and Brandeis 1890,
p. 195) but is not easy to define because of multidimensional
characteristics (Castañeda and Montoro 2007). Researchers
classified privacy into four types: physical, interactional,
psychological and informational (Laufer and Wolfe 1977,
Burgoon et al. 1989, Buchanan et al. 2007, Paine et al.
2007). The focus of this research is on information pri-
vacy implications brought about by the advancement of
information technology.
Information privacy is ‘the ability of the individual to
personally control information about one’s self’ (Stone et al.
1983, p. 460). IPC exist when an individual feels threat-
ened by a perceived unfair loss of control over their privacy
by an information-collecting body. The concerns can also
be stated as ‘an individual’s subjective views of fairness
within the context of information privacy’ (Malhotra et al.
2004, p. 337). The concerns are based on an individual’s
own perception and values so people may perceive con-
cerns differently even in the same context (Donaldson and
Dunfee 1994). Different environmental factors (e.g. indus-
try, culture, laws) may also result in differences in perceived
privacy concerns (Malhotra et al. 2004).
Researchers have developed measures to assess IPC
(Smith et al. 1996, Stewart and Segars 2002, Dinev and
Hart 2004, Malhotra et al. 2004, Buchanan et al. 2007).
Malhotra et al. (2004) expanded upon the concerns for infor-
mation privacy (CFIP) framework of Smith et al. (1996)
to develop the Internet user’s information privacy con-
cerns (IUIPC) model to explain Internet user disclosure
behaviour. The CFIP and IUIPC were limited to a corpo-
rate evaluation framework. Research is needed to examine
individual perceptions of IPC.
2.2. Privacy calculus theory
Privacy calculus theory is a fundamental theory of pri-
vacy concerns that is applied to understand an individual’s
information providing or disclosing behaviour. Laufer and
Wolfe (1977) first introduced the ‘calculus of behaviour’ in
terms of privacy, defined as an assessment which compares
benefits and risks of information disclosure. Individuals
generally expect that their personal information will be
used for legitimate purposes and do not desire negative
consequences. Individuals expect economic value or social
benefits at the expense of losing control of their personal
information (Culnan and Armstrong 1999). Individuals
calculate the results of information disclosure and decide
whether to exchange personal information. Individuals per-
forming this calculus expect at least a balanced outcome
and are more likely to provide personal information and
accept the loss of privacy the greater the anticipated positive
outcome (Culnan and Bies 2003).
Researchers have investigated the factors that influence
the privacy calculus behaviour amongst a number of differ-
ent parties engaged in information exchange: employees
and organisations, consumers and corporations, Internet
users and Internet services (Dinev and Hart 2004). Dinev
and Hart (2006) extended the privacy calculus model to an
e-business environment finding that contextual differences
should be taken into account when dealing with privacy
concerns for Internet users. An individuals’ privacy risk
perception differs from others according to an individual’s
own complex process of privacy calculation. Dinev and
Hart (2006) found that trust and interest significantly miti-
gate privacy risk perceptions, which can also influence an
individual’s information disclosing behaviours. An exam-
ination of the factors that influence privacy calculation is
necessary.
2.3. Antecedents of information privacy concerns
The antecedents of IPC can be divided into four groups: fac-
tors related to information-collecting bodies, factors related
to information-providing individuals, environmental fac-
tors and factors related to the information itself. Several
studies have shown that IPC are influenced by the activities
of information-collecting bodies, including the collection,
improper access, unauthorised secondary use and errors
in personal information (Stone et al. 1983, Culnan 1993,
Smith et al. 1996, Campbell 1997, Stewart and Segars 2002,
Malhotra et al. 2004). Inappropriate technical and man-
agerial controls to protect personal information are also
collector’s factors (Wang et al. 1998).
Campbell (1997) suggested that individuals’ IPC may
vary according to their negative personal experiences,
knowledge of actual corporate policies and socioeconomic
status. Trust in a company is undermined and concerns over
personal information being exploited increase if an indi-
vidual has had a previous negative experience with data
inaccuracies or with the misuse of personal information by
a company. Smith et al. (1996) have empirically shown
that personal negative experiences significantly increase
IPC.
Knowledge of corporate practices and of the technology
that manages consumer information can affect individu-
als’ privacy concerns. When people are not knowledgeable
about a company’s collecting practices, people tend to be
more concerned about individual privacy concerns (Nowak
and Phelps 1992). People not familiar with information
technology have greater privacy concerns because of an
increased risk perception about the technology (Dinev
Behaviour & Information Technology 47
and Hart 2006, Paine et al. 2007). Age and level of
education also influence IPC. People of a lower socioe-
conomic status, lower educational level and of a greater
age are likely to have higher IPC (Wang and Petrison
1993, Milne et al. 1996, Campbell 1997). Individual
personality factors such as trust/distrust, paranoia and
social criticism are also associated with IPC (Smith et al.
1996).
Environmental factors such as laws, regulations and
sociocultural norms can influence one’s IPC (Chen et al.
2008). Milberg et al. (2000) empirically proved that
four types of group norms (individualism/collectivism,
masculinity/femininity, power distance and uncertainty
avoidance) influence privacy concerns. The type and
amount of information requested are also related to pri-
vacy concerns (Culnan and Armstrong 1999, Hann et al.
2007, Paine et al. 2007). Individuals tend to have greater pri-
vacy concerns when sensitive or specific information (e.g.
financial, medical, demographic information) is requested
(Malhotra et al. 2004).
2.4. Counterbalancing factors and monetary rewards
Information-collecting companies normally compensate
users for providing personal information and attempt to
make collected data secure to reduce IPC. Firms pub-
licise their privacy policy online because self-regulatory
efforts can build user trust and decrease user IPC (Phelps
et al. 2000, Bellman et al. 2004, Awad and Krishnan
2006, Hann et al. 2007, Hui et al. 2007). Adopting
new information technology (e.g. encryption technology)
is another approach organisations use to alleviate IPC
(Hung and Wong 2009). Most information-collecting
companies attempt to mitigate user concerns by offer-
ing compensation for personal information. Compensation
takes two forms: monetary rewards and non-monetary
rewards. Monetary rewards include cash and other forms
of physical gifts, whereas non-monetary rewards are intan-
gible benefits like personalised services (Taylor et al.
2009).
Offering monetary rewards is a common method to min-
imise response errors and increase the participation rate
in surveys. Previous studies have empirically proven the
effectiveness of the monetary rewards for respondent par-
ticipation (Church 1993, Helgeson et al. 2002, Cobanoglu
and Cobanoglu 2003). Hansen (1980) conversely argued
that the effect of monetary rewards on response quality is
not clear as the effectiveness of the rewards varies across
different sample groups and research areas. Higher levels
of monetary rewards may stimulate information-concealing
behaviour from respondents (Bentley and Thacker 2004).
O’Neil and Penrod (2001) found that offering monetary
rewards causes response errors because rewards motivate
individuals to participate simply to obtain the monetary
reward.
2.5. Behavioural outcomes of information privacy
concerns
Increased concerns of an organisation’s information-
collecting practice can influence individual’s behavioural
responses. The privacy calculus perspective suggests that
individuals tend to show information privacy-protective
responses when they expect unbalanced outcomes or
perceive threats to their privacy. Son and Kim (2008)
categorised negative responses into three broad types: infor-
mation provision (refusal and misrepresentation), private
action (removal and negative word-of-mouth) and pub-
lic action (complaining directly and indirectly). Refusal
and misrepresentation are the primary responses individ-
uals exhibit to protect their privacy (Milne and Boza 1999).
Individuals can also request to remove their personal infor-
mation from a database when a person feels that their
privacy is threatened (Smith et al. 1996). An individual can
share privacy concerns to salient others. Negative word-of-
mouth may damage an information-collecting companies’
reputation (Son and Kim 2008).
3. Conceptual framework and hypotheses
This study focuses on how monetary rewards influence IPC
and related behavioural outcomes. The characteristic of the
information requested is an important factor that influences
IPC. Several studies report that information sensitivity is
the primary factor to influence IPC (Castañeda and Montoro
2007, Yang and Wang 2009). Monetary rewards are often
utilised to alleviate privacy concerns and persuade indi-
viduals to disclose information online. Information refusal
and misrepresentation are the primary responses individuals
demonstrate when they have IPC. Information sensitivity
and monetary rewards are employed as the main inde-
pendent variables, whereas privacy concerns, information
providing and misrepresentations are the dependent vari-
ables examined in this study. Figure 1 illustrates the study’s
conceptual framework.
3.1. Information sensitivity
Previous research defines information sensitivity as ‘the
level of privacy concern an individual feels for a type of
data in a specific situation’ (Weible 1993, p. 30). Informa-
tion sensitivity and IPC are conceptually similar in that one
Information
sensitivity
Privacy concerns,
Disclosure,
Misrepresentation
Monetary rewards
Antecedent
Moderator
Outcomes
Figure 1. Conceptual framework.
48 H. Lee et al.
can be defined by the other and both account for subjective
risk perceptions which may vary from person to person.
People request that sensitive information tend to have
higher privacy concerns because releasing the information
is perceived to be riskier than non-sensitive information
(Malhotra et al. 2004). Increased concerns from informa-
tion sensitivity can drive negative behavioural responses.
Negative responses consist primarily of either informa-
tion refusal or misrepresentation (Son and Kim 2008). This
study hypothesizes:
H1a: Information sensitivity increases information privacy con-
cerns.
H1b: Information sensitivity decreases information providing
intention.
H1c: Information sensitivity increases information misrepresen-
tation intention.
3.2. Monetary rewards
Monetary reward effectiveness has been studied in various
research domains. Monetary rewards are often used to facil-
itate survey recruitment and motivate participation among
individuals who might otherwise not respond (Singer and
Bossarte 2006). Studies employing mail and online surveys
have shown that monetary rewards significantly increase
response rates and decrease dropout rate (Church 1993,
Helgeson et al. 2002, Cobanoglu and Cobanoglu 2003,
Göritz 2006). Risk-benefit or utility theory posits that mon-
etary reward is the main reason that individuals disclose
information (Xie et al. 2006). Information privacy stud-
ies suggest that a reward encourages people to exchange
their personal information with fewer IPC (Sheehan and
Hoy 2000, Faja 2005, Hann et al. 2007, Hui et al. 2007).
Monetary rewards influence information providing and mis-
representation intentions (Malhotra et al. 2004, Son and
Kim 2008). This study hypothesizes:
H2a: Monetary reward decreases information privacy concerns.
H2b: Monetary reward increases information providing intention.
H2c: Monetary reward decreases information misrepresentation
intention.
3.3. Interaction effect between information sensitivity
and monetary rewards
Monetary rewards can not only increase response rates but
also increase response errors (O’Neil and Penrod 2001). The
privacy calculus theory suggests that a respondent receiving
a monetary reward from an information-collecting company
will evaluate the cost of disclosing personal information
against compensation. Respondents’ will be reluctant to
provide personal information if there is a large gap between
costs and benefits. Monetary rewards may stimulate risk
perceptions and elevate respondents’ concerns for disclos-
ing sensitive and important information. Andrade et al.
(2002) found that the offer of a reward is not an effective way
for managing individual’s concerns and may strengthen the
negative effect of privacy concerns on behavioural inten-
tions. An interaction effect between monetary rewards and
information sensitivity on IPC and behavioural intentions
may be expected. This study hypothesizes:
H3a: A significant interaction effect between information sen-
sitivity and monetary rewards on information privacy concerns
exists.
H3b: A significant interaction effect between information sensi-
tivity and monetary rewards on information providing intention
exists.
H3c: A significant interaction effect between information sen-
sitivity and monetary rewards on information misrepresentation
intention exists.
4. Method
4.1. Research design
An experimental survey was conducted to test the effect of
monetary rewards. Four types of web pages imitating a well-
known Korean e-commerce company were developed for
the experiment to appear authentic. Following an approach
utilised by previous research, web pages Type I and II
included low sensitive information, whereas web pages
Type III and IV had high sensitive information (Culnan
1993, Phelps et al. 2000, Sheehan and Hoy 2000, Andrade
et al. 2002). Monetary rewards for participants using Type
II and IV included a $10 coupon and inclusion in a lottery
drawing for mobile devices.
An online pilot test was conducted with 66 university
students in Korea to examine the validity of each scenario
during March 2011. Respondents reported their perceived
sensitivity to the information requested and perception of
the amount of monetary rewards to verify the experimen-
tal setting of each case. Paired t-tests were conducted and
indicated that perceived sensitivity was significantly differ-
ent between the low (Types I and II) and high (Types III
and IV) sensitive cases; the mean of low sensitive cases
were 2.67 while the mean of high sensitive cases were 6.18
(t=−10.595, p<.001). Monetary rewards cases (Types
II and IV) were also significantly different compared with
cases with no monetary rewards (Type I and III); the mean
of no monetary rewards cases was 1.49, whereas the mean of
monetary rewards cases was 5.23 (t=−11.388, p<.001).
The experimental design was statistically valid but a few
piloted respondents reported that an excessive amount of
sensitive information was asked for in the high sensitive
cases and that the amount of monetary reward was rela-
tively small for the monetary cases. One of the sensitive
information questions was discarded (card limit informa-
tion) and the amount of monetary reward allocated was
adjusted. The final web page types with four different con-
ditions are shown in Figure 2. Participants were asked to
complete an online questionnaire after accessing and using
one of the randomly assigned pages.
Behaviour & Information Technology 49
Figure 2. Four types of web pages.
4.2. Measurement instrument
The questionnaire measurement items were designed to
assess information privacy concerns (IPC), information
misrepresentation intention (IMI) and information provid-
ing intention (IPI) as dependent variables. Additional data
were collected as independent variables, including manip-
ulation check variables and demographical variables, to
facilitate additional analysis (Malhotra et al. 2004, Ahmad
and Mykytyn 2012). The variable ‘information privacy
concerns’ was assessed using four items introduced by
Dinev and Hart (2004), while Malhotra et al. (2004)’s
items were adopted for IMI and the IPI. The mean val-
ues of the multi-item constructs were used in the analysis
model. A trust value about the data collecting company
was measured for additional analysis by modifying Dinev
and Hart (2006)’s construct. Dummy variables (0: No, 1:
Yes) for information sensitivity and monetary rewards were
incorporated to assess the sincerity of survey respondents.
Respondent answers that were consistent with their given
case were retained in the study (e.g. Monetary rewards?
‘Yes’ for Types II and IV), while contradictory answers
disqualified respondents from the analysis. Table 1 lists the
measurement items used in this research.
4.3. Data
This study examined the negative effects of monetary
rewards on both online IPC and online information dis-
closure. General Internet users were solicited to participate
in this study by an online survey agency to minimise sam-
pling bias and to generalise the research results. The agency
distributed the survey to randomly selected members from
a database and membership points were provided for sur-
vey participants. Four hundred ten samples were collected
during a 2-week period in April 2011. Inappropriately
answered questionnaire responses were eliminated through
a data-filtering process utilising the marker variables, result-
ing in 370 usable responses. The number of subjects for
each of the four scenarios ranged from 91 to 94. Over 80%
of respondents had earned an associate’s degree or higher.
Most respondents have used the Internet for more than 6
years. The respondents were able to understand this study’s
context because they had extensive experience with Inter-
net services and an appropriate knowledge background. A
chi-square homogeneity test was performed to check for
sample differences between groups. The test statistic was
not significant for all outcomes confirming that every group
is homogeneous. Table 2 lists the sample characteristics and
the result of the homogeneity test.
5. Results
5.1. Manipulation check and reliability test
An independent sample t-test was conducted for the
manipulation check. Perceived information sensitivity and
perceived monetary incentive were used to confirm each
scenario’s difference. The low sensitive cases (Types
I and II; mean =3.65, SD =1.09) had a lower per-
ceived information sensitivity than the high sensitive cases
(Types III and IV; mean =5.45, SD =1.16). The t-test
shows that the information sensitivity cases were well estab-
lished and distinguished (t-value =−15.38, p<.001).
The scenario cases also showed significant differences
for monetary rewards (no monetary reward: mean =2.44,
SD =1.06; monetary rewards: mean =4.82, SD =0.98;
t-value =−22.50, p<.001). All manipulations appear to
have significant differences.
Multiple items were used to measure the three con-
structs (IPC; IPI; IMI), so the reliability and convergent
validities were tested using Cronbach’s α, Composite Reli-
ability (CR) and Average Variance Extracted (AVE). The
Cronbach αvalues of three constructs were relatively high
(IPC =0.95, IPI =0.92, IMI =0.86). Constructs are reli-
able when the CR is over 0.70 and AVE is over 0.50
(Bagozzi and Yi 1988). Each construct exceeded the CR and
AVE thresholds (CR: IPC =0.96, IPI =0.94, IMI =0.90;
AVE: IPC =0.87, IPI =0.81, IMI =0.70). Table 3 lists
50 H. Lee et al.
Table 1. Measurement instruments.
Variable Description
Dependent variable
Information privacy concerns
(IPC)
Given this experimental scenario
IPC1: I am concerned that the information I submit to the E-Commerce website could be misused
IPC2: I am concerned that other people or company can access private information about me to the
e-commerce website
IPC3: I am concerned about submitting information the e-commerce website, because of what others
might do with it
IPC4: I am concerned about submitting information the e-commerce website, because it could be
used in a way I did not foresee
(1. Strongly disagree ...7. Strongly agree)
Information providing
intention (IPI)
Given this experimental scenario, specify the extent to which you would reveal the information to
the e-commerce website
IPI1: 1.Very unlikely ...7. Very likely
IPI2: 1. Not probable ...7. Probable
IPI3: 1. Impossible ...7. Possible
IPI4: 1. Unwilling ...7. Willing
Information misrepre-
sentation intention
(IMI)
Given this experimental scenario, specify the extent to which you would falsify some of your
personal information
IMI1: 1.Very unlikely ...7. Very likely
IMI2: 1. Not probable ...7. Probable
IMI3: 1. Impossible ...7. Possible
IMI4: 1. Unwilling ...7. Willing
Information disclosure Will you accurately provide requested information to the e-commerce website? (0. No; 1. Yes)
Independent variable
Sensitivity of information Is requested information from the website generally sensitive? (0. No; 1. Yes)
Perceived sensitivity of
information
The sensitivity of request information from the website is? (1. Very low ...7. Very high)
Monetary rewards Does the e-commerce company offer monetary rewards for your personal information? (0. No; 1.
Yes)
Perceived monetary rewards The amount of monetary rewards for your information is? (1. Very little ...7. Very much)
Company trust I trust the e-commerce company? (1. Strongly disagree ...7. Strongly agree)
E-commerce trust I think the e-commerce environment is trustworthy in general (1. Strongly disagree ...7. Strongly
agree)
Perceived privacy breach How frequently have you personally been the victim of what you felt was an improper invasion of
privacy?
(1. Very infrequently ...7. Very frequently)
Media exposure How much have you heard or read about the use and potential misuse of the information collected
from the Internet?
(1. Not at all ...7. Very much)
Biographical variable
Gender 0. Female; 1. Male
Age 1. Teenager ...5. Over 50s
Educational background 1. High school ...5. PhD
Internet experience 1. Less than 1 year ...7. Over 6 years
E-commerce frequency 1. Not at all ...7. Almost everyday
reliability and validity results. The scenario design, manip-
ulation as well as constructs’ reliability and validity were
appropriate for hypothesis testing.
5.2. Hypotheses testing
The proposed hypotheses were tested using two-way
ANOVA. Three ANOVAs were performed for each of the
four different cases in which IPC, IPI and IMI were the
dependent variables. Table 3 lists the mean score of each
dependant variable.
Table 3 indicates that misrepresentation intention was
high in high sensitivity cases (Types III and IV), and IPI was
high in low sensitivity cases (Types I and II). Individuals
tend to have positive IPI and weak misrepresentation inten-
tion when the sensitivity of requested information is low.
The mean value for IPC was over 5 in all cases although it
was relatively high in the high sensitivity cases. Requesting
information itself may increase IPC. The results listed in
Table 4 indicate statistical differences between the vari-
ables. The first ANOVA test on IPC provided support for
H1a and H3a, but not for H2a. Information sensitivity had a
Behaviour & Information Technology 51
Table 2. Sample characteristics and homogeneity test between groups.
Total Type I Type II Type III Type IV
Category (N=370) (n=92) (n=94) (n=91) (n=93) χ2p-Value
Gender 0.53 0.91
Male 188 (50.8%) 44 49 46 49
Female 182 (49.2%) 48 45 45 44
Age 18.18 0.11
10–19 1 (0.3%) 0010
20–29 43 (11.6%) 16 10 6 11
30–39 183 (49.5%) 44 49 42 48
40–49 103 (27.8%) 29 22 31 21
50+40 (10.8%) 3 13 11 13
Educational background 14.51 0.27
High school 65 (17.6%) 19 17 15 14
Associate’s degree 69 (18.6%) 14 22 18 15
Bachelor’s degree 198 (53.5%) 45 46 48 59
Master’s degree 36 (9.7%) 12 9 10 5
Doctoral degree 2 (0.5%) 2000
Internet experience 24.07 0.15
Less than 6 years 14 (3.8%) 6422
Over 6 years 356 (96.2%) 86 90 89 91
E-Commerce frequency 16.13 0.58
Not at all 3 (0.8%) 0210
1–2 times per year 16 (4.3%) 4453
1–2 times per half year 24 (6.5%) 5928
1–2 times per three months 52 (14.1%) 13 15 10 14
1–2 times per month 158 (42.7%) 43 38 41 36
1–2 times per week 102 (27.6%) 23 21 31 27
Almost everyday 15 (4.1%) 4515
Perceived privacy breach 19.75 0.35
1 (Not at all) 34 (9.19%) 7 8 11 8
2 56 (15.14%) 9 14 13 20
3 84 (22.70%) 18 23 21 22
4 64 (17.30%) 16 15 17 16
5 113 (30.54%) 34 33 26 20
6 18 (4.86%) 7137
7 (Very much) 1 (0.27%) 1000
Media exposure 22.16 0.26
1 (Not at all) 16 (4.32%) 5524
2 19 (5.14%) 4465
3 43 (11.62%) 8 12 13 10
4 77 (20.81%) 19 19 24 15
5 151 (40.81%) 40 38 37 36
6 57 (15.41%) 11 16 8 22
7 (Very much) 7 (1.89%) 5011
Table 3. Mean score comparison for dependant variables.
Alpha CR AVE Type I (n=92) Type II (n=94) Type III (n=91) Type IV (n=93)
IPC 0.95 0.96 0.87 5.29 (1.16) 5.00 (1.30) 5.45 (1.09) 5.69 (1.05)
IPI 0.92 0.94 0.81 4.29 (1.00) 4.20 (1.00) 3.43 (1.09) 3.76 (1.38)
IMI 0.86 0.90 0.70 3.85 (1.06) 3.68 (1.06) 4.33 (1.17) 4.23 (1.34)
Notes: Type I: low sensitivity ×no monetary rewards, Type II: low sensitivity ×monetary rewards, Type III: high
sensitivity ×no monetary rewards, Type IV: high sensitivity ×monetary rewards. IPC: information privacy concerns,
IPI: information providing intention, IMI: information misrepresentation intention. The values provided are mean
(standard deviation).
52 H. Lee et al.
Table 4. ANOVA results.
DV Source SS DF MS Fp-Value
Information privacy concerns (IPC) Main effects
IS 16.41 1 16.41 12.35 .000
MR 0.07 1 0.06 0.05 .825
Interaction effect
IS ×MR 6.46 1 6.46 4.86 .028
Error 486.55 366 1.33
Information providing intention (IPI) Main effects
IS 38.64 1 38.64 30.44 .000
MR 1.37 1 1.37 1.08 .299
Interaction effect
IS ×MR 3.99 1 3.99 3.14 .077
Error 464.64 366 1.27
Information misinterpretation intention (IMI) Main effects
IS 24.50 1 24.50 18.11 .000
MR 1.73 1 1.73 1.28 .259
Interaction effect
IS ×MR 0.16 1 0.16 0.12 .735
Error 494.99 366 1.35
Notes: DV, dependant variable; SS, sum of squares; DF, degree of freedom; MS, mean square; IS, information
sensitivity; MR, monetary rewards.
5.8
5.6
5.4
5.2
5.0
4.8
4.4
4.2
4.0
3.8
3.6
3.4
No Monetary Reward Monetary Reward No Monetary Reward Monetary Reward No Monetary Reward Monetary Reward
4.4
4.2
4.0
3.8
3.6
3.4
Type I
Type II
Type III
Type IV
Type I
Type II
Type III
Type IV
Type IV
Type I
Type II
1. Information
privacy concerns
(IPC)
2. Information
providing intention
(IPI)
Type III
3. Information
misinterpretation
intention (IMI)
Figure 3. Dependent variable graphical comparisons.
significant main effect on IPC (F=12.35, p<.001), while
monetary rewards did not. A significant interaction effect
between information sensitivity and monetary rewards on
privacy concerns exists (F=4.86, p<.05).
The second test showed similar results in that both
H1b and H3b were supported, but not H2b. Informa-
tion sensitivity had a significant main effect on IPI (F=
30.44, p<.001) and an interaction effect between infor-
mation sensitivity and monetary rewards on IPI (F=3.14,
p<0.1) was marginally significant. The p-value of H3b
was over .05, but it was still within a 90% confidence
interval using a two-tailed test. Only information sensitiv-
ity influenced IMI (F=18.11, p<.001) and there was no
significant interaction effect. H1c was supported while H2c
and H3c were rejected.
Figure 3 shows the results in a graphical format. There
were significant differences in the dependent variables for
most cases with respect to information sensitivity. Individ-
uals experienced stronger IPC while exhibiting lower IPI
and a higher misrepresentation intention when highly sen-
sitive information was requested. Monetary rewards had
no significant effect by itself on every dependent variable,
but there were significant interaction effects with IPC and
IPI. Monetary rewards alleviate IPC when information with
low sensitivity was requested. Monetary rewards in contrast
increase concerns when information with high sensitivity
Behaviour & Information Technology 53
was requested. Monetary rewards were positively related
with the intention to provide information for information
with high sensitivity, while intention to provide informa-
tion declined slightly for information with low sensitivity.
No significant interaction effect between monetary rewards
and information sensitivity on IMI exists.
5.3. Additional analysis
Figure 3 illustrates that individuals exhibit greater IPC and
have a stronger intention to provide personal information
at the same time when information with high sensitiv-
ity was requested with monetary rewards. Individuals also
exhibit less IPC and have a weaker intention to provide
personal information at the same time when information
with low sensitivity was requested without any reward.
These contrasting results indicate that an individual’s deci-
sion to provide personal information may be associated
with additional factors. A stepwise binomial logistic regres-
sion model was developed to deepen the understanding of
which factors affect information-disclosing behaviour. The
model considers several factors like perceived sensitivity of
information, perceived monetary rewards and demographic
information to calculate the probability that the person
discloses his or her personal information without distortion.
Logit (π) =α+β1PSI +β2PMR +β3CT +β4ECT
+β5PRB +β6ME +β7GEN +β8AGE
+β9ECF
where dependent variable =information disclosure (0:
No, 1: Yes), αis the constant, βithe coefficient i, PSI
the perceived sensitivity of information, PMR the per-
ceived monetary rewards, CT the company trust, ECT the
E-commerce trust, PRB the perceived privacy breach, ME
the media exposure, GEN the gender, AGE the age, EDU
the education, ECF the E-commerce frequency.
Pearson’s Chi-square statistic indicates an adequate fit
for every model in Table 5 and the Nagelkerke’s R2of
model 3, an alternative form of R2for the binomial logis-
tic regression model, was higher than other models. The
probability of information disclosure was dependent on
perceived sensitivity of information (β=−0.58, p<.01),
perceived monetary rewards (β=0.14, p<0.1), company
trust (β=0.52, p<.01), e-commerce trust (β=0.24, p<
.05) and e-commerce frequency (β=0.25, p<.05). Per-
ceived sensitivity of information was the most influential
independent variable in every model. The significance of
perceived monetary rewards was not consistent as the β
of perceived monetary rewards was relatively lower than
the βof other variables. Company trust, e-commerce trust
and media exposure demonstrated a significant relationship
with the dependent variable. E-commerce frequency, one of
the demographic variables, significantly affected informa-
tion disclosure. The overall predicted power of the model
was 74.6%.
Table 5. Binomial logistic regression for information
disclosure.
Variable Model 1 Model 2 Model 3
Perceived sensitivity
of information
−0.53∗∗∗ −0.56∗∗∗ −0.58∗∗∗
Perceived monetary
rewards
0.17∗∗ 0.13 0.14∗
Company trust 0.54∗∗∗ 0.52∗∗∗
E-commerce trust 0.23∗∗ 0.24∗∗
Perceived privacy
breach
−0.07 −0.11
Media exposure −0.20∗∗ −0.20∗∗
Gender −0.06
Age −0.06
Education 0.11
E-commerce
frequency
0.25∗∗
Constant 1.12 −0.64 −0.64
−2 loglikelihood 435.64 396.26 390.30
Degrees of freedom 2 6 10
Chi-square 48.80∗∗∗ 88.18∗∗∗ 94.14∗∗∗
Pseudo-R2
(Nagelkerke)
0.17 0.29 0.31
N370 370 370
∗Significant at the 10% level.
∗∗Significant at the 5% level.
∗∗∗Significant at the 1% level with two-tails test.
6. Discussion
6.1. Key findings
Monetary rewards do not always work efficiently to col-
lect information online. Monetary rewards work differently
in varying contexts while information sensitivity shows
consistent effects. Information sensitivity was a significant
antecedent of information privacy concerns and related
behaviours, whereas monetary rewards had no statistical
effect. Hypothesis 3a results support the contention that
offering monetary rewards can increase the individuals’
information privacy concerns when information with high
sensitivity is requested. The reward for sensitive infor-
mation makes individuals worry about the motives of
information-collecting companies. Rewards may remind
individuals of direct or indirect negative experiences of
privacy violation. People may think that the information-
collecting bodies have improper intentions, such as unau-
thorised secondary use and abuse of personal information.
The research results are consistent with the previous studies,
which indicate that the reward does not diminish informa-
tion privacy concerns and raises the awareness of the value
of an individual’s information (Bentley and Thacker 2004,
Taylor et al. 2009).
Individuals’ intention to provide sensitive information
was higher when a monetary reward exists than when
no reward exits. Subjects had a stronger intention to
provide personal information that was highly sensitive
54 H. Lee et al.
despite increasing information privacy concerns attributed
to monetary rewards. Offering physical gifts undermines
an individual’s intrinsic motivation and induces a person
to focus on the extrinsic motivation. O’Neil and Penrod
(2001) found that offering monetary rewards can change
the respondent’s motivation to provide personal informa-
tion from a pure purpose to an impure purpose of receiving
the reward. Hypothesis 2c and 3c results support this con-
tention that monetary rewards do not guarantee the quality
of information because the difference between misrepre-
sentation intention with and without monetary rewards
was not significant. Intention of distorting personal infor-
mation remains unchanged or becomes higher due to
increased information privacy concerns. Information col-
lection activities will generate useless and inaccurate data
when the intention of distorting personal information is
high combined with a strong intention to provide per-
sonal information. Monetary rewards in contrast alleviate
privacy concerns during the collection of less sensitive
information. Figure 3 illustrates that monetary rewards mit-
igates information privacy concerns and misrepresentation
intention.
IPI coupled with monetary rewards was strong but
slightly declined. This was an unexpected result but may
indicate that IPI is not only related with the sensitivity and
rewards. A stepwise binomial logistic regression model was
examined to understand this result which emphasised the
importance of trust for information-collecting companies.
Trust was a significant antecedent to personal informa-
tion disclosure as well as information sensitivity. Previous
research suggests that building trust is an effective way
to persuade individuals to disclose personal information
(Milne and Boza 1999, Dinev and Hart 2006, Eastlick et al.
2006, Joinson et al. 2006, Taylor et al. 2009). Trust is an
important factor for fostering customer satisfaction and loy-
alty in e-commerce systems and transactions (Kim and Lee
2002, Horn et al. 2005). The results suggest that trust also
facilitates personal information sharing in an e-commerce
environment.
E-commerce frequency had a significant effect on infor-
mation disclosing behaviour in the regression model. Infor-
mation disclosing behaviour is associated with familiarity
with the environment in which personal information is
requested. Individuals familiar with an e-commerce envi-
ronment may know very well how their information will
be used and familiarity is positively associated with trust
(Gefen 2000).
Media exposure was also a significant antecedent, while
perceived privacy breach was not. People who have been
previous victims of privacy breaches may feel that the dam-
age was not as serious as expected and find the incident
to be a common occurrence. On the other hand, people
exposed to privacy victimisation incidents in the media but
have not been previous victims of privacy breaches may
hesitate to provide their personal information because the
inexperience might cause a disproportionately stronger risk
perception.
6.2. Theoretical and practical implications
This study deepens the understanding of online informa-
tion privacy concerns and disclosure behaviour. One of the
main contributions is the findings about the effectiveness of
monetary rewards. While many studies find that monetary
rewards generally have a positive effect on user’s infor-
mation privacy concerns and related behaviours, this study
presents a different perspective.
Monetary rewards have negative effects on information
privacy concerns in certain situations. A monetary reward
intensified information privacy concerns, which suggests
that individuals consider monetary rewards as ‘decoys’.
Andrade et al. (2002) expected that offering rewards would
decrease concerns about self-disclosure, but found that the
opposite was true. Faja (2005) found that individuals are
more concerned when they disclose information for finan-
cial rewards than for other benefits. This study confirms the
findings of Andrade et al. (2002) and Faja (2005) and offers
an explanation of their results. Requests for sensitive infor-
mation increases an individual’s apprehension and raises
doubts about the motives behind monetary reward offers.
This study also explains why previous research on
the effects of monetary rewards towards information pri-
vacy concerns has inconsistent results. Monetary rewards
can either mitigate or intensify information privacy con-
cerns depending on the context. Both positive and negative
relationships are observed between monetary rewards and
privacy concerns in previous studies (e.g. Andrade et al.
2002, Taylor et al. 2009). Yang and Wang (2009) in contrast
found that there is no direct effect of monetary rewards on
privacy concerns. The study results are consistent with pri-
vacy calculus theory and related studies in that individuals
respond to monetary rewards in complicated ways. A sim-
ple linear relationship does not exist for monetary rewards
between consumer’s privacy concerns and intention to pro-
vide information. This study’s results are similar to findings
in previous studies examining the relationship between
trust and behavioural outcomes (e.g. Culnan and Armstrong
1999, Eastlick et al. 2006, Taylor et al. 2009), which reaf-
firms the importance of trust. Building trust and developing
a close relationship with consumers is a more effective way
to collect high-quality information than offering monetary
rewards.
This study also provides practical implications about the
effect of monetary rewards for collecting personal informa-
tion. Information-collecting companies should design their
reward mechanisms prudently as monetary rewards exhibit
a negative influence on information privacy concerns. Com-
pensation should be based on the type of information
requested. Monetary rewards are appropriate to collect data
when requesting general information as an individual’s
Behaviour & Information Technology 55
intention to distort information is relatively low. Engender-
ing an individual’s trust to decrease perceptions of risk (e.g.
established privacy policy, transparent usage of collected
information, etc.) is more appropriate to collect sensitive
information. Demographic factors are also important to con-
sider when requesting information. Sensitive information
requests are appropriate for loyal users who have multi-
ple previous transactions with the information collecting
company or are familiar with the e-commerce environment.
Experienced individuals exhibit a high intention to dis-
close sensitive information, which is in contrast to novice
individuals.
6.3. Limitations and future research
This study sought to explain the unexpected and contra-
dictory findings from Andrade et al. (2002) and Yang and
Wang (2009). The focus of this study was on the possi-
ble negative effects that monetary rewards have on both
information privacy concerns and information disclosure.
Low and high information contexts as well as contexts
in which monetary rewards were provided and others in
which they were withheld were simulated to examine the
research hypotheses. Additional contexts are appropriate to
pursue in future research (e.g. reputation, variety of rewards,
requests of specific types of information, etc.) to gain a
more complete understanding of the complex processes
that influence online behaviours. This research will reveal
ways to satisfy both information-collecting companies and
information-providing individuals.
Multiple variables identified in the literature were not
considered in this study, like user’s dispositional factors,
privacy policy, technology issues and collector’s indus-
try. These variables also affect privacy concerns but were
not addressed because they were beyond the scope of this
study. The majority of study respondents were Korean.
Koreans normally have access to the Internet and have sig-
nificant experience using e-commerce. Respondents from
additional countries, cultures and industries might produce
different results. Future research should extend the study
to include respondents who are representative of additional
contexts. Cross-country, cross-cultural and cross-industrial
comparisons may possibly provide a deeper understanding
of user perceptions and behaviours.
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