1. law, govt and politics
2. legal issues
3. civl rights
4. privacy

School of Business and Social Sciences, Aarhus University
Department of Business Administration
The role of privacy
in technology acceptance
The effect of privacy concern on intention to use E-commerce
Master Thesis
Author
: Jasper van Zwieten
Date
: March, 2015
Program
: Msc. Marketing
Supervisor
: Dr. Athanasios Krystallis
Characters
: 131.464
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
Abstract
This paper investigates the role that privacy can play in the consumer decisionmaking process for a privacy-sensitive technology. There is a great deal of ambiguity in
privacy literature regarding the manner in which privacy may affect technology acceptance.
This paper concerns itself with investigating discrepancies among works of privacy research
regarding technology acceptance, by means of established behavioral theories. This paper
focuses on the trade-offs between privacy concern and other determinants of E-commerce
acceptance. A conjoint analysis is conducted to find the extent to which privacy, security,
control, perceived usefulness, perceived ease-of-use and price can influence the intention to
use a technology that presents privacy implications, i.e. E-commerce. Accordingly these
variables are investigated in light of the moderating variables: privacy attitude, involvement,
and trust, in order to find how privacy concern can affect the intention to use an Ecommerce site.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
Table of Contents
1
Introduction .............................................................. Fout! Bladwijzer niet gedefinieerd.
1.1
2
3
Research question ............................................. Fout! Bladwijzer niet gedefinieerd.
Literature review ............................................................................................................2
2.1
The concept of privacy ............................................................................................2
2.2
Perceived behavioral control ...................................................................................4
2.3
Privacy and attitude ................................................................................................5
2.4
Privacy trade-offs ....................................................................................................8
2.5
The role of involvement .................................... Fout! Bladwijzer niet gedefinieerd.
2.6
The role of trust ....................................................................................................14
Theoretical framework and methodology .....................................................................15
3.1
Conceptual model .................................................................................................15
3.2
Experimental design ..............................................................................................16
3.3
Independent variables...........................................................................................18
3.4
Privacy and the determinants of behavioral intention ...........................................21
3.5
Moderating variables ............................................................................................23
3.5.1 Privacy attitude .....................................................................................................23
3.5.2 Involvement ..........................................................................................................26
3.5.3 Trust .....................................................................................................................29
4
5
Data collection and analysis ..........................................................................................31
4.1
Pre-test .................................................................................................................31
4.2
Data collection ......................................................................................................32
4.3
Respondent base ..................................................................................................32
4.4
Conjoint analysis ...................................................................................................35
4.5
Factor analysis ......................................................................................................37
4.6
Bivariate correlation ..............................................................................................38
4.7
Reliability and cluster analysis ...............................................................................40
4.8
Analysis of variance ...............................................................................................40
4.9
Independent T-test ........................................... Fout! Bladwijzer niet gedefinieerd.
Conclusion and discussion ........................................ Fout! Bladwijzer niet gedefinieerd.
5.1
Recommendations for future research ..................................................................46
6
References ....................................................................................................................47
7
Appendices ............................................................. Fout! Bladwijzer niet gedefinieerd.6
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
1.
Introduction
Privacy is a complex subject in consumer behavior research. While privacy has been
the subject of academic examinations for roughly a century, the investigated roles it may
play in the consumer decision-making process are largely unsubstantiated (Smith et al.,
2011). Classic consumer decision-making literature explains that consumers can search for
products after they notice a stimulus that evokes a specific need or desire (Pelsmacker et al.,
2010). For example, in the E-commerce context it could be the case that consumers have
trouble finding a product, and want to have access to a more extensive assortment of
products. Or consumers are unhappy regarding the website’s privacy conduction, and want
to feel more secure regarding privacy protection. Privacy could act as a bottom-line
requirement that consumers want fulfilled in order to make use of the technology. But
referring to privacy as a “bottom-line” requirement might be a bit excessive. Because even
consumers who state to be highly concerned regarding privacy have been found to engage in
behavior that entails privacy implications, despite their stated concerns (Acquisiti and
Grossklags, 2005).
The rise of the information age has caused consumer concerns regarding privacy to
be more evident than ever before (Smith et al., 2011). An American consumer poll showed
that 72% of consumers are concerned regarding privacy by means of corporate surveillance
of their online behavior (Consumers-Union, 2008). The same consumer poll showed that
82% of consumers are concerned regarding security by means of credit card data theft.
Privacy has been found to be the most important factor in consumer evaluations of Ecommerce by a substantial margin (Schaup and Belanger, 2005). Regardless, E-commerce
sales are increasing rapidly. In the U.S. E-commerce sales accounted for 4% of total sales in
2009 (U.S. Census Bureau, 2012), in respect to 1,7% of total sales in 2004 (Schaup and
Belanger, 2005). Despite an abundance of consumer concerns regarding E-commerce
privacy, the popularity of E-commerce is rising rapidly.
The effect of privacy perceptions on consumer behavior has not been properly
established by former privacy research (Norberg et al., 2007; Smith et al., 2011). Privacy is a
complex concept that requires sophisticated techniques to be properly examined (Malhotra
et al., 2004). Therefore, this paper will review the role of privacy in technology acceptance
literature. This paper will focus on technology acceptance via intentions to use an Ecommerce site, due to the availability of literature regarding this context and its tangibility to
consumers. This paper intends to find to what extent stated privacy concerns can affect
behavioral intention in a technology acceptance context, through investigating the role of
privacy in the decision-making process. In order to investigate the role of privacy in the
decision-making process, this paper will focus on the manner in which privacy perceptions
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
may satisfy consumer needs or desires in the evaluation of alternatives. To be more specific,
this paper will focus on the extent to which this evaluation can affect behavioral intention, in
light of established behavioral theories.
1.1
Research question
How can privacy concern affect the intention to use an E-commerce website?
2.
Literature review
2.1
The concept of privacy
Privacy literature has not yet properly established the role privacy can play in the
consumer decision-making process (Smith et al., 2011). When we look at privacy in a
technology acceptance context, the most apparent problem is that different works of privacy
research investigating similar notions consider privacy to play a different role in their models
(Breward, 2007; Drennan et al., 2006; Featherman and Pavlou, 2002; Liu et al., 2005; Martins
et al., 2013; McCole et al., 2010; Pavlou, 2003; Shin, 2010; Vijayasarathy, 2004; Ward et al.,
2005). A myriad of conceptualizations regarding what privacy entails and inclines has
resulted in a stream of recent literature that simply attempts to classify our knowledge of
privacy (Lanier and Saini, 2008; Malhotra et al., 2004; Smith et al., 2011). These
classifications expose parameters of the concept of privacy that help to distinguish a
definition of privacy, which can be universally used in empirical investigations of its effects. It
is important that we have a unified view of what privacy is, so that it can be used as a
foundation for current and future research. With a clear definition of privacy it can be
meaningfully related to established constructs such as attitude or behavior, the results of
privacy research can be replicated and build upon, and our knowledge on the effects of
privacy can grow. Marketing research in specific is appointed to understand the factors in
the consumer decision-making process, and must therefore gain a clear understanding of
what privacy is and what role it can play.
Unfortunately, different works of literature maintain different definitions for the
concept of privacy. Naturally research in different streams of literature (i.e. law, sociology or
business) view privacy from different perspectives, but even in the context of consumer
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
behavior the concept remains ill-defined. We can see a multitude of perspectives through
which privacy can be investigated; e.g. privacy as a subset of perceived risk (Breward, 2007;
Featherman and Pavlou, 2002; Zhou, 2012), privacy as uncertainty (George, 2002; Pavlou et
al., 2007) or privacy as a security concern (Dallaway, 2007; Ward et al., 2005). For example,
former research regarding perceived privacy risk refers to a fear of losing the data a
consumer provides to online services, namely falling victim to criminal activity in the form of
identity theft (Featherman and Pavlou, 2003). This exposes a security aspect of privacy that
can ultimately be related to a perceived financial risk. However, privacy in the traditional
sense is not necessarily related to this risk. There is a psychological component to privacy
that is innate in human nature. It can be found in our modern society as well as in primitive
cultures, and even in animals (Westin, 1967). This psychological component refers to the
need for a degree of seclusion from social interactivity (Lanier and Saini, 2008). But the type
of privacy concern that is currently most apparant is related to the disclosure and
accessibility of personal information, i.e. information privacy. From a consumer perspective,
information privacy involves the consumer’s sense of anxiety regarding the perceived
exposure and/or accessibility of personally identifiable information (Lanier and Saini, 2008;
Smith et al., 2011).
Information privacy is not necessarily limited to the type of personal information that
can be gathered with, for example, online registration forms. Personal information can refer
to something as simple as an individual’s age, but also to information that is a bit more
difficult to find, e.g. individuals’ biometric data, personal conversations or their product
preferences. But privacy investigations are naturally limited to the techniques available at
the time, so the vast majority of contemporary privacy research focuses on the type of
information that is typically registered for use of online services. Since the security aspect of
privacy is heavily intertwined with this type of personal information gathering, it is
understandable that a great deal of research ignores the distinction between privacy and
security. Security is generally considered to be a main category of consumer’s privacy
concerns (Lanier and Saini, 2008). But security and privacy are two different concepts. When
a company has completely secured the conduction of personal information from other
parties, the company itself can still choose to violate consumer privacy by inappropriately
handling the personal information (Culnan and Williams, 2009). The lack of this distinction
may provide some insight as to why privacy research shows different results for the
investigated manners in which privacy can effect behavior (Breward, 2007; Tan et al., 2012;
Lee and Song, 2013; Smith et al., 2011; Zhou, 2012). This will be discussed in further sections
of the literature review.
The investigation of the behavioral effects of privacy is something that only recent
works of literature are concerned with (Smith et al., 2011). But former privacy research has
found it difficult to investigate actual behavioral effects of privacy concern (Smith et al.,
2011). This may be attributed to privacy being a difficult concept to measure. To account for
this, Smith et al. (1996) have proposed the variable ‘privacy concern’ as a construct related
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
to an individual’s anxiety regarding information sharing. The measurement of privacy
concern has helped researchers to conduct empirical studies on the abstract concept of
privacy. Regardless, there is a problem associated to privacy research that makes it difficult
to investigate the relation between respondents’ stated privacy concern and their behavior
(Acquisiti, 2004; Acquisiti and Grossklags, 2005; Jensen et al., 2005). Norberg et al. (2007)
refer to a ‘privacy paradox’, where consumers may voice concern regarding the conduction
of privacy in privacy research, but perform behavior that presents privacy implications
despite their stated concerns. A great deal of privacy research may therefore be incorrect in
assuming that respondents’ stated behavioral intentions for engaging in privacy-sensitive
behavior are valid indicators of their behavior. Consumer privacy research would benefit
greatly from a deeper understanding on the behavioral effects of privacy. Unfortunately, it is
common practice for researchers to investigate the effects of stated privacy concerns on the
dependent variable ‘behavioral intention’ (Smith et al., 2011). This presents a problem for
contemporary privacy research, since a significant portion of our knowledge regarding
privacy may not be valid outside of the research context, and thus cannot be build upon.
Therefore, this paper will investigate the relation between privacy concern and behavioral
intention in light of established behavioral theories.
2.2
Perceived behavioral control
Smith et al. (2011) explain that former privacy research generally presupposes from
the Theory of Reasoned Action (Fishbein and Ajzen, 1975) that behavioral intention can
significantly reflect behavior. Theoretically, this assumption should be a reliable, but if we
further investigate it in light of the constructs of this behavioral theory and privacy concern
we may gain some insight in the problem of this reasoning. Ajzen (1991) identified that
behavioral intention is not a reliable predictor of behavior if consumers perceive they do not
have the ability to perform the desired behavior. Consumers are not only considered to
engage in a specific type of behavior when they perceive it to be the most desirable
alternative, but also because they perceive to have a level of control over attaining the
desired behavioral outcome. Ajzen (1991) constructed the Theory of Planned Behavior as an
improvement of the Theory of Reasoned Action with the addition of the variable ‘perceived
behavioral control’. Perceived behavioral control reflects the extent to which consumers
believe that they are capable of performing the desired behavior. The variable was derived
from self-efficacy theory (Bandura, 1977), but while self-efficacy focuses largely on the
individual’s own perceived aptitude of performing a task, perceived behavioral control takes
external factors into account as much as internal factors. Perceived behavioral control can
therefore be quite a significant explanatory variable for the relation between privacy
concern and behavior. After all, an old study exposed that a majority of consumers (81%)
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
believe that consumers in general have no control over the conduction of their personal
information (Budnitz et al., 1998).
The aspect of perceived control in relation to personal information sharing has
commonly been integrated in the investigated variable privacy concern (Lanier and Saini,
2008; Smith et al., 2012). The view of privacy concern as the individual’s perceived control
over the use and distribution of personal information can be dated back as early as the study
of Westin (1967). Perceived control is considered to be a subset of privacy concern since a
perceived lack of control over the conduction of personal information adds to the sense of
anxiety a consumer can feel in respect to privacy (Lanier and Saini, 2008; Phelps et al., 2000).
As such, privacy literature identifies control as a main category of consumers’ privacy
concerns, and commonly investigates control as a subset of the variable privacy concern
(Lanier and Saini, 2008; Martin, 2012; Phelps et al., 2001; Tan et al., 2012; Smith et al., 2012).
But the perceived ability to control information is not necessarily a type of privacy concern,
at least for the definition of privacy as a psychological concept related to information anxiety
(Sheehan and Hoy, 2000; Dinev and Hart, 2004), but could act as a separate variable with a
distinguished effect on behavioral intention (Phelps et al., 2000). Control has also been
suggested to act as an antecedent of privacy concern (Culnan and Armstrong, 1999), but this
relation could not be empirically validated (Dinev and Hart, 2004). Nevertheless, decades of
research that view control as a subset of privacy concerns indicate that the two constructs
clearly are related. However, to the best of our knowledge this relation, or more precisely
this distinction, between the two constructs has not been considered in light of the Theory
of Planned Behavior. This might explain the discrepancy between stated privacy concerns of
respondents and their observed behavior. When respondents state they perceive to have no
control over the conduction of their personal information, privacy concern is generally noted
to be higher. However, according to the Theory of Planned Behavior this infers that these
respondents’ stated behavioral intentions with regards to privacy-sensitive decisions are less
predictive of their behavior (Ajzen, 1991).
2.3
Privacy and attitude
Contemporary privacy literature generally investigates the discrepancy between
behavioral intention and behavior through the concept of bounded rationality (Acquisiti and
Grossklags, 2005; Norberg and Horne, 2007; Smith et al., 2012). It is nearly impossible for a
consumer to acquire and consider all information relevant to making a decision (Simon,
1982). It may be that many consumers engage in behavior that presents privacy implications
while they are not adequately aware of these implications, or simply unable to think of these
implications while making the decision. However, Acquisiti and Grossklags (2005) found that
even when consumers are able to process sufficient information that they are still likely to
engage in behavior with known privacy implications, despite their stated concerns. Acquisiti
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
(2004) explains that this may be the case because of hyperbolic discounting, which inclines
that consumers discount the possible future negative effects of engaging in privacy-sensitive
behavior over the immediate benefits. In other words, situational or environmental cues
weigh more importance in consumers’ evaluations of whether to engage in behavior than
their previously held beliefs and related risk perceptions. This exposes that in general the
attitude formation related to privacy concern might be weak (Holland et al., 2002).
An attitude encompasses the individual’s beliefs and emotions toward an attitudinal
object (Fazio, 1986). The concept of attitude specifically refers to the evaluation of an
attitudinal object as derived from held beliefs, e.g. providing personal information always
leads to a loss of privacy, or emotions, e.g. the anxiety related to privacy concern. (Ajzen and
Fishbein, 2000, Fischer and De Vries, 2008; Loewenstein et al, 2007; Slovic et al, 2007). For
example, in the evaluation of the attitudinal object: an E-commerce site that presents
privacy implications, a consumer who is highly concerned regarding privacy is assumed to
evolve a negative attitude towards the site, ceteris paribus. If the attitude is strong and can
be retrieved from the consumer’s memory prior to the behavior, it should serve as a reliable
indicator of behavior (Fazio, 1995). Therefore, the findings of Acquisiti and Grossklags (2005)
show that in general the attitude formation related to privacy concern may be weak. To the
best of our knowledge, former research has not investigated the relation between attitude
strength and privacy concern. Incorporating the concept of attitude strength could however
provide insight as to why the privacy paradox exists. This will be explicated further in this
section.
Literature on the antecedents of privacy concern has not properly established which
factors influence the individual’s privacy concern, i.e. the proposed factors are not
repeatedly confirmed in subsequent studies (Smith et al., 2011). Naturally factors such as
personality differences (Lu et al., 2004; Bansal et al. 2010), demographic differences (Culnan
and Armstrong 1999; Sheehan and Hoy, 2000) or culture (Milberg et al., 2000) can provide
insight as to why one person’s privacy concern differs from another, but the only factor that
can certainly influence the importance of privacy concern in the general consumer decisionmaking process is knowledge. After all, privacy concern regarding a technology can only be
triggered if the consumer is aware that the technology presents privacy implications
(Cespedes and Smith, 1993). When consumers are aware that a technology could present
privacy implications in some manner or form, they still do not necessarily have to consider
these privacy implications to be a threat to their privacy. Consumers have a defined general
attitude towards privacy based on their psychology and value-system, but their expectations
of how privacy ought to be conducted depends on the technology (Martin, 2012). For
example, social network sites such as Facebook are known to share the information of users
and their acquaintances with third parties, which in a different context would likely be seen
as a privacy violation (Hull et al., 2010). These context-dependent expectations are the result
of personal characteristics as well as the perceived attributes of the technology, but are
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
influenced by experiences with privacy violations as well as by the evaluation of information
regarding the technology’s privacy implications (Martin, 2012).
Smith et al. (1996) found that experience with privacy violations has a strong effect
on privacy concern. The attributional theory of motivation (Weiner, 1985; Weiner, 2000),
shows that judgments of what caused the emotional response to a former behavioral
outcome can affect future attitudes toward performing similar behavior, it stands to reason
that the attitude formation derived from privacy violations is likely to be strong, given that
they have evoked a strong enough emotional reaction. However, for this to happen the
consumer must receive some form of feedback on the violation (Fazio et al., 2004). Due to
the intricate nature of current data-gathering techniques, many occurrences that consumers
could consider to be privacy violations remain unnoticed. Research also shows that positive
cues are perceived as more significant in the evaluation of a behavioral outcome (Pierro et
al., 2004). In other words, consumers may generally weigh the perceived gains of former
privacy-sensitive behavior as more important than the perceived negative outcomes. In
addition, since privacy violations often occur a long period of time after the consumer has
engaged in behavior, the consumer may not even make a connection between the two
(Norberg and Horne, 2007). These considerations indicate that, while a percentage of people
certainly exists who have formed negative attitudes as the result of experience with privacy
violations, the majority of consumer privacy attitudes regarding a specific technology are
most likely formed through the evaluation of related information.
Negative information, such as is presented by the contemporary popularized media
coverage on privacy violations, is a stronger indicator of an attitude formation than positive
information (Ajzen, 2001; Norberg and Horne, 2007). It is therefore not surprising that many
respondents state to be negative towards privacy conduction in aforementioned works of
privacy research. In addition, attribution theory explains that consumers can have affective
responses in their evaluation toward future consequences of engaging in behavior (Weiner,
2000). For example, a service that requires someone to enter a telephone number before
they can make use of the service could invoke a negative emotional response toward the
service, since consumers might perceive that they could receive unwanted phone calls in the
future. There is no such thing as privacy benefits for engaging in behavior with privacy
implications, i.e. engaging in behavior that requires the consumer to provide personal
information can never protect privacy more than simply not engaging in this behavior.
Therefore individuals are believed to generally base their emotional response to perceived
consequences on evaluations of negative information, such as is presented in the media.
This inclines that consumer attitudes toward general privacy conduction are either negative
or neutral. A case could be made that consumers who have formed a negative attitude
toward general privacy conduction will have a negative attitude toward a specific attitudinal
object with privacy implications as well. However, a general attitude is not a strong predictor
of specific behavior (Schiffman et al., 2009). The findings of Acquisiti and Grossklags (2005)
may be attributed to the fact that this research investigated the behavioral effects of general
7
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
privacy concern. But even an attitude related to a specific attitudinal object is not necessarily
predictive of behavior (Schiffman et al., 2009). Fazio and Zanna (1978) explain that this
relation can be better explained when attitude strength is included as a variable.
A strong attitude is signified by several associations to relevant object evaluations
(Fazio, 1986). For example, individuals who signed up for a social network and accordingly
received unwanted e-mails, phone calls and advertisements, saw their pictures distributed
on other websites and have friends who experienced similar privacy violations, will likely
have a stronger attitude toward the social network’s privacy conduction than individuals
who have only encountered one of these occurrences. Privacy concern will be more deeply
embedded in the mind of consumers with a strong attitude toward privacy. Lavine et al.
(1998) show that a strong attitude is more easily retrieved when an individual evaluates
whether to engage in behavior, whereas a weak attitude is more likely to be formed at the
time of this evaluation. As Holland et al. (2002, p. 869) explain: “strong attitudes guide
behavior, whereas weak attitudes follow behavior in accordance with self-perception
principles.” In other words, a person with a strong negative attitude towards privacy
conduction may choose not to engage in behavior with privacy implications, whereas a
person with a weak negative attitude towards privacy conduction may engage in this
behavior and accordingly adjust their privacy attitude (e.g. I chose to offer my personal
information, hence privacy may not be so important to me) or adjust their view on the
perceived privacy implications of the attitudinal object (e.g. I offered my personal
information on a website that the media has portrayed as privacy endangering, but the
website is probably more reliable than it is portrayed).
2.4
Privacy trade-offs
Support for this can be found in cognitive dissonance theory (Festinger, 1956).
Cognitive dissonance is defined as the psychological discomfort that arises when the
consumer holds two contradictory beliefs when evaluating an attitudinal object (Festinger,
1956; Elliot et al., 1994), e.g. signing up for an e-commerce site saves money and is therefore
desirable, but presents a privacy risk and is therefore undesirable. Cognitive dissonance
typically causes the consumer to try to reduce this discomfort through justification of one of
these beliefs, while ignoring information that supports the contradictory belief (Festinger,
1956; Elliot et al., 1994). The concept of cognitive dissonance indicates there may be a tradeoff between privacy concern and the attributes of the attitudinal object. If consumers are
highly concerned regarding privacy, it is more likely that they will discount the perceived
benefits that the attributes of the attitudinal object provide than consumers who indicate
low privacy concern, who will more likely discount their privacy considerations. On the other
hand, it is also possible that consumers who indicate high privacy concern may discount their
privacy considerations in order to avoid cognitive dissonance. For example, in the
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
investigation of the effect of privacy concern in a social networking context, Tan et al. (2012)
found that ‘perceived usefulness’ was a stronger determinant of behavioral intention for
respondents who indicated high privacy concern than for respondents who indicated low
privacy concern.
‘Perceived usefulness’ is a variable from the Technology Acceptance Model (Davis,
1985) that refers to the degree to which consumers believe that the technology will provide
benefits in performing related activities. The Technology Acceptance Model investigates the
effects of the independent variables ‘perceived usefulness’ and ‘perceived ease-of-use’ (a
variable that reflects the degree to which the consumer believes that using the technology is
effortless) on behavioral intention (Davis, 1985). Originally, the TAM investigated these
effects through the mediating variable attitude, but it was found that excluding this variable
did not take away any of the TAM’s explanatory power (Davis, 1989). The TAM has been
confirmed to be a valid model in various technology investigations (Davis, 1989; King and He,
2006; O’cass and Fenech, 2003). Nevertheless, it is not necessarily a reliable predictor for the
acceptance of every technology due to its parsimony (Straub and Burton-Jones, 2007;
Venkatesh, 2000). However, broader models such as the Unified Theory of Technology
Acceptance (Venkatesh et al., 2003; Venkatesh et al., 2012) have the opposite problem of
including too many determinants of technology acceptance, which do not have to be
significant in every context (Attuquayefio and Addo, 2014). While the TAM may be too
generalized to be applied to every technology acceptance case, the constructs ‘perceived
usefulness’ and ‘perceived ease-of-use’ have been stated to capture all relevant beliefs
related to general technology acceptance (Benbasat and Barki, 2007). It has therefore
become common practice to add to the validity of the model by including variables that are
known to play a role in the acceptance of the specific technology that is being investigated
(Ha and Stoel, 2009). However, this does not necessarily alleviate other problems associated
with the TAM. A problem of the TAM is that because of the manner in which constructs are
presented to respondents, studies that investigate technology acceptance with this model
are vulnerable to common methods variance (Straub and Burton-Jones, 2007). In other
words, respondents may answer questions regarding the dependent variable ‘behavioral
intention’ based on previous answers to questions regarding the independent variables
‘perceived ease-of-use’ and ‘perceived usefulness’. If consumers’ actual behavioral
intentions differ from their perceptions regarding e.g. usefulness, these consumers may
state to have a behavioral intention that does in fact align with their previously stated
perceptions in order to avoid cognitive dissonance in their reasoning (Straub and BurtonJones, 2007). The TAM can provide more explanatory power when respondents are asked to
evaluate specific behavioral choices, e.g. “Which E-commerce site would you be most likely
to shop from?”, rather than inquiring the more general notion of whether the respondent
would engage in behavior (Kowalewski et al., 2013; Vijayasarathy, 2004). The common
method variance associated with the TAM can be alleviated by integrating this theoretical
model in a Conjoint Analysis.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
The Expectancy-Value theory (Fishbein, 1963) was one of the first theories to state
that consumers evaluate objects based on a set of beliefs. Each of these beliefs contains an
importance score in the individual’s evaluation process, through which we can determine
the relative prominence of each belief in the consumers’ evaluation of an object. An
effective method for finding the importance of these beliefs in relation to specific products
or services is Conjoint Analysis (Green and Wind, 1975). Conjoint Analysis uses ordinary least
squares estimations to measure the consumers’ evaluations of a set of alternatives with
specified attributes and attribute levels, in order to find the consumers’ utility functions for
each alternative as an aggregate of the part-worth utilities for the attributes. Utility refers to
the perceived ability to satisfy consumer needs or desires, and part-worth utility refers to
the extent to which an attribute can satisfy consumer needs or desires. Conducting a
Conjoint Analysis is fairly representative of actual decision-making, since respondents are
asked to evaluate a set of alternatives. Particularly with the addition of a ‘cheap talk’ script,
i.e. a statement regarding the importance of filling in the questionnaire as realistically as a
real-life decision, the validity of this research method is increased (Carlsson et al., 2005).
Conjoint analysis makes no assumptions about the nature of- and relations between
independent variables, and is therefore a good tool for investigating the perceptions related
to these variables (Bajaj, 1998). Through Conjoint Analysis we can find the trade-offs
consumers make between privacy and the other attributes in their evaluation of the
alternatives. A problem of Conjoint Analysis however is that respondents can only properly
evaluate a limited number of attributes. Conjoint Analysis requires researchers to present a
relatively parsimonious choice-set in order for respondents to take all of the included
attributes in consideration. A general rule-of-thumb of Conjoint Analysis is that respondents
should not be presented with more than six attributes (Green and Srinivasan, 1978).
Therefore, not all perceptions that may influence technology acceptance in a specific case
can be addressed in a Conjoint Analysis. The validity of a Conjoint Analysis is therefore
dependent on the researchers who conduct it, since they have to choose the attributes that
are proposed to provide the most explanatory power for the respondent’s decision-making
process.
Privacy concern in a consumer context is generally considered to consist of the
categories ‘notification’ ‘control’ and ‘security’ (Lanier and Saini, 2008). The proposed
categories refer to the manner in which privacy conduction is perceived by the consumers.
For example, notification refers to consumer’s beliefs that they are properly informed on the
collection and use of personal information by the company (Lanier and Saini, 2008). This
paper concerns itself with investigating the importance of attributes related to the
psychological component of privacy, as well as security, control, perceived usefulness and
perceived ease-of-use, in order to reflect the most important considerations regarding
privacy in a technology acceptance context. The privacy attribute will include considerations
regarding the notification construct. The attributes are derived from privacy literature and
technology acceptance literature (i.e. Technology Acceptance Model) in relation to Ecommerce acceptance literature (Schaup and Belanger, 2005; Chen et al., 2010), and will be
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
further discussed in the section ‘Independent variables’. But even if the most detrimental
attributes for the research context are chosen, it is unlikely that they will reflect the full
extent of all perceptions related to an attitudinal object. The limitation regarding the
amount of attributes to be presented in a Conjoint Analysis research causes this method to
be dependent on the research context. But the study of Martin (2012) found that consumers
form judgments based on similar factors of similar importance in the evaluation of privacysensitive technologies across different contexts. Hence, this context-dependent method of
investigation can expose the role privacy concern plays in the consumer decision-making
process regarding a broader context than solely the specific context of this investigation.
In order to determine how privacy considerations can affect behavioral intention in a
technology acceptance context, we will also investigate how they affect the determinants of
behavioral intention. The principal-agent problem can provide insight on the effect of
privacy concern on these determinants. The principal-agent problem occurs when one party
(agent) holds more information than another party (principal) for which the first party
(agent) is capable of making decisions (Akerlof, 1970; Arrow, 1963; Eisenhardt, 1989). It is
therefore representative of the issue a consumer faces when offering privacy-sensitive
information to a company. Former research has related the principal-agent problem to
privacy concern in E-commerce (George, 2002; Pavlou et al., 2007). These works of research
have found significant relations between privacy concern and the formation of attitudes
regarding online purchasing, which affect behavioral intention. Pavlou et al. (2007) state that
privacy concern only influences attitudes regarding the company and not the product’s
perceived quality. Consumers who indicate high privacy concern can decide not to buy a
product regardless of quality perceptions. Perceived quality, as defined by Zheitaml (1988),
refers to the product attributes through which a consumer judges competitive advantage.
For example, when a consumer evaluates two products which are perceived as exactly
similar except for their perceived usefulness and perceived ease-of-use, the consumer will
most likely prefer the product that is perceived to provide the most performance benefits or
is the most effortless in usage. While privacy concern can certainly have an effect on
behavioral intention in this example, this effect is considered to relate to judgments
regarding the seller of the product rather than judgments regarding the product’s
advantages. This exposes that privacy concern does not have a direct effect on the
determinants of technology acceptance.
While privacy concern is unlikely to directly affect these determinants, as
aforementioned it may still play a moderating role in their effects on behavioral intention.
The research of Tan et al. (2012) found the following moderation effects: for privacy
concerned consumers ‘perceived usefulness’ is a stronger determinant of behavioral
intention and ‘perceived ease-of-use’ is a weaker determinant of behavioral intention, in
respect to consumers who are not concerned about privacy. However, the study of Im et al.
(2008), who investigated privacy as a subset of perceived risk across various types of
technology, found that these relations occur in the opposite direction. This discrepancy may
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
be explained by the difference in scope of the concepts privacy concern and perceived risk.
Whereas perceived risk is a broad concept including several risk attributes related to a loss
(e.g. financial or psychological), privacy concern as defined by Tan et al. (2012) refers to the
risk evaluation of privacy implications. Im et al. (2008) used former literature to find the
parameters of perceived risk. But former literature regarding perceived risk usually
investigates the effect of perceived risk as a whole rather than the individual effects of the
subsets of perceived risk (Featherman and Fuller, 2003; Im et al., 2008; Martins et al., 2013).
However, Featherman and Pavlou (2003) confirmed that there is a high degree of collinearity
among risks related to a physical loss, such as a financial loss, and among risks related to a
psychological loss, such as a loss of personally identifiable information. It could be that these
types of risk perceptions influence the decision-making process in different manners in
certain cases. Studies that investigate the effects of privacy and security separately show
that their effects can be different (Belanger et al., 2002; Pavlou et al., 2007; Vijayasarathy,
2004). Nevertheless, former research can regard privacy concern and security concern as
constructs which are too similar to be investigated separately (Urban et al., 1999), leading to
a lack of a distinction between the two constructs in privacy investigations (McCole et al.,
2010). Other works of privacy research that do distinguish between the two constructs tend
to investigate the effect of privacy- and security concern on a mediating variable (e.g.
anxiety, trust, perceived risk), and the effect of that variable on attitude or behavioral
intention (Pavlou et al., 2007; Zhou, 2012). This may be the case because former research
either cannot find a direct relation between privacy concern and indicators of acceptance
(McKnight et al., 2011; von Stetten et al., 2011; Tan et al., 2012), or states that the results of
such a finding bear no validity outside of the research context due to the privacy paradox
(Smith et al., 2011). However, the construct of perceived risk is not afflicted by the same
problem (Glover and Benbasat, 2011; Pavlou, 2003; Zhou, 2012). Perceived risk is a wellestablished construct that can affect the consumer in clear and distinct manners. The
financial risk associated to identity theft for example provides a clear causal relation
between providing personal information and a security violation. An invasion of privacy on
the other hand is less well-defined, and likely to be less well-understood. The study of
Belanger et al. (2002) found that a consumer’s perception regarding the implemented
security features on an e-commerce site is significantly more important for explaining
behavioral intention than the perceived presence of privacy seals or privacy statements.
However, perceived security features were also found to be significantly more important
than the presence of security statements (Belanger et al., 2002). These findings are
important to this paper for two reasons: Firstly, they indicate the significance of investigating
security and privacy as distinct constructs. Secondly, they indicate that we have to define the
privacy attribute as a clear and tangible construct, rather than the more common method to
be found in privacy research, which is to investigate the perceived importance of privacy
seals or statements. This will be further discussed in the section ‘Independent variables’.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
2.5
The role of involvement
The difference in the findings of the studies of Tan et al. (2012) and Im et al. (2008)
may also be explained by the context of investigation. Even though registering for a social
network remains to be an interesting area of investigation for privacy concern, the barriers
for this decision are relatively low compared to other online services. In comparison to
online services with higher perceived risk elements, the social networking context is one that
can be signified by relatively low involvement.
Involvement refers to the consumer’s perception of the product’s intrinsic
importance (Howard and Sheth, 1969). The Elaboration Likelihood Model (Petty and
Cacioppo, 1986) indicates that having the motivation to evaluate a decision can be just as
important as having the ability to evaluate a decision. If consumers are not involved with a
behavioral decision, they may simply base their behavior on emotionally appealing stimuli
rather than a carefully considered evaluation of the available information (Petty et al., 1983).
On the other hand, when consumers are highly involved in the purchase decision, they are
more likely to actively search and analyze relevant information in order to avoid making the
wrong decision. Highly involved consumers form attitudinal changes through found and
retrieved information aimed at cognitive justification, rather than emotional justification.
The strong attitudinal change associated with this cognitive justification process is a more
reliable predictor of behavior than the weak attitudinal change that ensues from the
evaluation of emotionally appealing stimuli (Petty et al., 1983). If a consumer is highly
concerned regarding privacy, but not involved in the specific behavioral decision that entails
privacy implications, the consumer may not care to find, retrieve or analyze information
regarding these privacy implications. Therefore, privacy concern may not be a reliable
predictor of behavior if we ignore the role of involvement. Unfortunately, the role of
involvement is rarely investigated in privacy literature. Akhter (2014) found that consumers
who are involved with the internet as a whole are generally less concerned regarding
privacy. Pavlou et al. (2007) found that purchase involvement plays a moderating role
between the uncertainty of engaging in online shopping behavior (to which privacy concern
is a subset) and behavioral intention, in the sense that it strengthens its negative relation.
But to the best of our knowledge, former research has not investigated the difference in the
direct relations between privacy concern and behavioral intention for consumers who
indicate different levels of involvement.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
2.6
The role of trust
Another variable that could affect the privacy-behavior relation is trust. We can
relate trust to privacy concern through ‘moral hazard’. The theory of moral hazard indicates
that the seller of a privacy-sensitive product may be inclined to inappropriately handle the
consumer’s private information if they have a financial incentive, since these actions are
difficult to observe by the consumer (Holmstrom, 1979). If the consumer is aware of this
when evaluating the purchase decision, anxiety may arise. This anxiety results from a lack of
trust towards the information provided by the seller before purchase and their perceived
actions post-purchase (Akerlof, 1970; Arrow, 1963). The literature has investigated the
relation between privacy and trust in several manners (Breward, 2007; Smith et al., 2011).
Former literature often cannot find a direct relation between privacy concern and behavioral
intention (McKnight et al., 2011; von Stetten et al., 2011; Tan et al., 2012). Trust is suggested
to act as a mediating variable, i.e. privacy concern affects trust, which in turn affects
behavioral intention (Breward, 2007; Eastlick et al., 2006; Smith et al., 2011). There is strong
support for these relations in the context of e-commerce (Liu et al., 2005). But the literature
exposes that the relation between privacy concern and trust is not necessarily a one-way
stream (Breward, 2007; Pavlou et al., 2007; Schoenbachler and Gordon, 2002; Smith et al.,
2011). Trust can mitigate the concerns a consumer has regarding privacy (Pavlou et al., 2007;
Schoenbachler and Gordon, 2002). Companies can easily signal that they handle private
information in an acceptable manner, which would generally help to alleviate privacy
concerns (Culnan and Armstrong, 1999; Milne and Boza, 1999; Xu et al., 2010). If consumers
trust E-commerce companies, than their privacy concerns can be mitigated by these signals
(Milne and Boza, 1999; Pavlou et al., 2007). Therefore, stated privacy concerns may not be
significant predictors of behavioral intention for consumers who trust E-commerce
companies.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
3.
Theoretical framework and methodology
3.1
Conceptual model
Figure 1 portrays the proposed conceptual model that will be used to investigate the
role of privacy in the decision-making process regarding technology acceptance in an Ecommerce context. A total of 18 hypotheses are investigated, which are conglomerated into
4 hypotheses for clarity purposes. The conglomerations relate to the hypotheses’ main
variables of investigation. The variables, their proposed relations, and methods of
investigation will be defined in the following sections.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
3.2
Experimental design
This paper will present respondents with a number of profiles regarding alternatives
of a hypothetical E-commerce website. E-commerce is a rather broad concept, but any
specific characterization of the website of investigation (e.g. online clothing retail, gadgets or
specialized goods) may cause respondents to evaluate the profiles based on a set of specific
privacy expectations that are not necessarily representative of E-commerce in general.
Extensive E-commerce websites, such as Amazon.com, could prove to be applicable to an
investigation of this nature. However, due to their presence in the mind of the consumer
these might induce bias. The companies may already have an established association with
privacy in the mind of the consumer, for example due to the company image or media
attention (Morran, 2011). Or different effects related to the company could distort the
accuracy of the investigated relationship, e.g. country of origin. The presented profiles will
not contain any brand names or distinguishing factors other than the attributes related to
privacy concern and technology acceptance, i.e. the presented profiles are nothing more
than combinations of the attributes. Behavioral intention therefore refers to the intention to
use a combination of these attributes in the evaluation of E-commerce websites. Through
this manner of investigation we focus on the attributes’ relative explanatory power of
behavioral intention.
We use a traditional conjoint analysis in order to discover the relative importance of
the attributes. A choice-based conjoint analysis or an adaptive conjoint analysis can arguably
reflect the consumer decision-making process in manners that are even more representative
of actual decision-making (Backhaus et al., 2007). However, since the focus of the analysis
lies with the attributes and their relative importance for explaining behavioral intention, a
traditional conjoint analysis is chosen. Eight profiles regarding alternatives for an Ecommerce site will be presented to respondents (see appendix B), so they can rate their
likeliness to use them. Rating methods in Conjoint Analysis are effective for finding choice
probabilities of the alternatives, and giving quantitative representations of the importance
of the investigated attributes for predicting behavioral intention (Louviere, 1998). The partworth utilities of the attributes will reflect how important consumer perceptions related to
privacy concern and determinants of technology acceptance are, when consumers evaluate
alternatives for a technology that present privacy implications. With the part-worth utilities
we can determine the coefficients of the independent variables, i.e. the quantitative value of
the attribute for determining utility. The coefficients will accordingly be tested for a set of
moderating variables that are proposed to influence the relations between these attributes
and behavioral intention.
Each of the attributes will contain two levels for consumers to evaluate. It can be
argued that more levels would allow us to find more predictive estimations of the
investigated relations, however, two levels were chosen for the following reasons: Firstly,
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
two levels for the attributes allows us to keep the profiles parsimonious. Six attributes with
two levels can be investigated through a fractional factorial design of eight profiles. Hence,
all respondents can evaluate all profiles of the fractional factorial design without bias
induced from the required number of evaluations. Secondly, this design is both balanced and
orthogonal, thus it can accurately investigate main-order effects. Thirdly, two levels for the
attributes allows us to keep the presentation of information regarding the attributes
relatively parsimonious as well, thus increasing the likelihood that consumers will take all
presented information into account. A case can be made that we should include more levels
for the important variables of investigation, as well as for the metric variable of price.
However, this might induce bias through the ‘number-of-levels effect’, inclining that
respondents would assign more weight to attributes with more levels (Steenkamp and
Wittink, 1994). In addition, even adding one extra level (or two levels in order to keep the
design balanced) to an attribute would incline that we have to investigate a fractional
factorial design of 16 alternatives. This is considered too many alternatives for all
respondents to give reliable estimates. There are manners to reduce the number of
presented alternatives, such as removing profiles that represent trivial choices, for example
a profile that has none of the innovations and no price reduction. Another method is to
create a block design where the presented profiles of the full experimental design are
divided among respondents. However, these methods can respectively have problems
regarding the reduction of the orthogonality of the design, or the reliability of the
measurement depending on the heterogeneity of respondents. Particularly since we will
investigate the variables in light of the respondent base’s heterogeneity (see section:
Moderating variables), we propose to investigate no more than two levels for each attribute.
The hypothetical manner of investigation presents consumers with relatively novel
innovations to an E-commerce site. This inclines that the evaluations consumers make will
be representative of more general technology acceptance cases. Respondents are more
likely to rely on evaluations of the alternatives rather than on heuristics in order to make a
decision, so we can effectively investigate the proposed trade-offs. However, the technology
can only be perceived as novel in relation to existing E-commerce sites. The respondent base
will only consist of individuals who have shopped online in some manner or form. This allows
us to define parameters for the proposed attributes, as well as reduce biased results from
individuals who do not have enough prior knowledge to make an evaluation in this context,
or for who the subject of investigation is simply not personally relevant. Due to the familiar
setting, consumers are presumed to have established expectations regarding how privacysensitive information ought to be conducted (Martin, 2012). Therefore respondents are
considered to evaluate the attributes based on established expectations regarding Ecommerce privacy conduction, that are not limited to the hypothetical context of
investigation. The investigated trade-offs are considered to bear validity outside of the
research context and may be extended beyond the hypothetical scope of this research.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
3.3
Independent variables
In order for this research to representatively reflect the effect of privacy perceptions
in the consumer decision-making process, we must determine attributes that are
detrimental for these perceptions in an e-commerce context. This paper distinguishes the
psychological component associated with privacy from control and security perceptions, and
investigates these three categories of privacy concern as distinct attributes. An important
consideration in the presentation of these attributes is that it must have a neutral tone-ofvoice. Since respondents are susceptible to social desirability bias in privacy investigations,
they are assumed to be influenced by emphasis on benefits or drawbacks of the presented
attributes.
To representatively reflect the trade-offs between privacy, security, control and the
determinants of technology acceptance, we must also determine attributes that are
detrimental for perceived usefulness and perceived ease-of-use in this context. Lastly, in
order find meaningful trade-offs we must develop representative profiles that include the
notion of price. This section will explicate the proposed attributes.
Privacy will be investigated as software that collects and distributes browsing habits.
We address the most detrimental dimension of privacy concern investigations, outside of its
control, security and awareness aspects, by focusing on the collection of personal
information (Lanier and Saini, 2008; Malhotra et al., 2004; Smith et al., 2011). Presenting
respondents with an attribute regarding personal information collection provides them with
awareness of privacy implications, and allows respondents to have the ability to make a
carefully considered evaluation on the importance of privacy for their decision. Thus we can
examine the effect of privacy on behavioral intention in light of the examination of Acquisiti
and Grossklags (2005), who found that even when consumers have the opportunity to
adequately process information, privacy concern may not play a significant role in the
decision-making process. However, if we were to make consumers completely aware of the
full extent of the privacy implications than we would ignore an important aspect of privacy
concern. Awareness itself is a passive dimension of privacy concern (Malhotra et al., 2004),
i.e. consumers can be concerned that they are not properly notified regarding the use of
their personal information (Lanier and Saini, 2008). We can address this dimension of privacy
concern in the privacy attribute with the inference that personal information will be
distributed to secondary parties. The original scale for measuring privacy concern
incorporates secondary use as a main dimension of privacy concern (Smith et al., 1996). By
including this inference respondents are more likely to connect the collection of personal
information to privacy implications, whereas if this is not included in the privacy attribute
respondents may assume that companies intend to collect personal information solely for
consumers’ benefit. Respondents may still assume that personal information is collected
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
solely for their benefit, but since no information associated to any benefit is provided this is
considered to reflect a low sense of anxiety regarding personal information. The type of
personal information that we investigate is browsing habits, i.e. consumer’s search
results/activity on the E-commerce site. Schaup and Belanger (2005) found that the
presence of ‘cookies’, i.e. browsing habit registration software, was significantly more
important to consumers’ evaluation of E-commerce site attributes than any other
determinant of privacy. We focus on the collection of browsing habits because it adheres to
a distinct definition of personal information that is not associated with security implications,
i.e. if the collection of browsing habits is important to consumers, this reflects that the
aforementioned psychological component of privacy is important to the consumer decisionmaking process rather than, for example, the sense of anxiety related to a possible financial
risk.
Security will be investigated as software that encrypts financial information. By
distinguishing financial information from personal information we provide a clear distinction
between the concepts of privacy and security. Information encrypting is a fairly wellunderstood concept that translates easily into an attribute. It has been found to be the most
important aspect of security for consumers who value security (Chen et al., 2010). An
attribute related to the encryption of data is more important in explaining behavioral
intention for consumers who are less knowledgeable regarding information technology with
regards to consumers who have expertise in this aspect, but this is believed to be the case
because consumers with expertise are capable of judging the security of a website for
themselves (Chen et al., 2010). Due to the nature of our research, respondents cannot judge
the security of the website by any other factor than the security attribute. Therefore the
security attribute is believed to be reflective of security perceptions for both consumers with
expertise and those who have little knowledge regarding security features.
Control will be investigated as software that enters codes in personal information
which allow users to restrain the use and distribution of this information. An important
consideration for the attribute is that it mentions that these codes are linked to the user
accounts, and that the involved companies have no control over it. In this manner of
investigation we reflect important considerations of the control aspect of privacy, namely
the importance for consumers to feel a sense of control over the collection and distribution
of personal information (Milne and Boza, 1999; Phelps et al., 2010). The control attribute
refers to the relatively vague term ‘personal information’ so that it is distinct from the terms
‘browsing habits’ and ‘financial information’ as examined in the privacy and security
attributes respectively. The most important consideration for the control attribute is that it
relates to the psychological component of the consumers’ need for control, and that it
reflects the ability to exert control in a privacy context. The generality of the term ‘personal
information’ may therefore help address the need for control regarding the general
conduction of personal information rather than address a need for control regarding specific
types of personal information.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
Perceived usefulness will be investigated as search software that allows the website
to have an extensive product assortment. There are a number of factors that may influence
the perceived usefulness of an e-commerce site, but the most important one for the stated
satisfaction of consumers is product assortment (Schaup and Belanger, 2005). While this
paper does not investigate satisfaction, consumer responses regarding e-commerce
determinants of satisfaction reflect that this attribute, in comparison to other presented
attributes, may be the most important attribute in their perception regarding why they
would use a particular e-commerce site in light of the alternatives. The findings of Schaup
and Belanger (2005) reflect that a consumer considers an extensive product assortment to
be the greatest benefit of the website (with the exclusion of privacy protection), specifically
when the product assortment contains seasonal and specialty goods. By presenting an
attribute that addresses the greatest perceived benefit of e-commerce sites we address the
most significant determinant of its perceived usefulness.
Perceived ease-of-use will be investigated as a user-friendly interface with improved
search capabilities that allow for easy location of products. Ease of shopping is undoubtedly
an advantage of e-commerce sites in comparison to traditional shopping methods, but in
comparisons of alternative websites consumers may only perceive subtle differences in
ease-of-use. The most reflective dimension of ease-of-use in comparisons of alternative
websites would be the user-friendliness of the interface. But particularly for consumers who
are convenience-oriented, the search capabilities of the site are deemed more important
than the interface (Chen et al., 2010). Therefore, in order to capture the largest explanatory
power for perceived ease-of-use, we integrate the user-friendliness of the interface with
enhanced search capabilities.
Price will be investigated as an investment in the distribution network that allows the
company to offer products for an average of 8% less than its main competitors. The cost of
products is undoubtedly an important factor for explaining behavioral intention in a
consumer technology acceptance context (Brown and Venkatesh, 2005; Venkatesh et al.,
2012). Even though the cost of products is not generally considered to be a detrimental
attribute in the evaluation of e-commerce sites (Chen et al., 2010; Schaup and Belanger,
2005), it is one of the most well-established variables known to have an effect in the
decision-making process. Price is traditionally considered as a perceived sacrifice that
consumers weigh against the perceived benefits of a product or service (Zheitaml, 1988),
and is therefore important for examining the trade-offs in the decision-making process. The
magnitude of the price differential has been decided upon based on informal interviews
conducted in the pilot study. Several respondents were asked if they considered other
respondents to base their answers solely on price, if these were presented with a price
differential of 5%, 10% or 15%. These findings showed that 5% was not considered a
noteworthy price differential, whereas 10% was already considered large enough for some
respondents to base their answers solely on price. In addition, if the price differential was
considered too extensive this may distort stated behavioral intentions, since the perception
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
of low prices can negatively influence quality perceptions (Dodds et al., 1991; Zheitaml,
1988). We account for this with a plausible reason for the price differential, i.e. an
investment in the distribution network.
3.4
Privacy and the determinants of behavioral intention
By implementing the independent variables in a conjoint analysis, this paper aims to
discover to what extent the psychological component related to privacy concern, and its
control and security aspects, can influence behavioral intention. When privacy is
investigated as an attribute in a conjoint analysis regarding the E-commerce context, it was
found that privacy is the most important factor in their evaluations by a substantial margin
(Schaup and Belanger, 2005). However, we present privacy as a distinguished tangible
attribute that respondents as to which consumers are required to evaluate its importance
for explaining behavioral intention in relation to other distinguished attributes. Rather than
investigating the perceived importance of privacy, we investigate the consumer utility of
mitigating the psychological anxiety related to information privacy. We propose that due to
the specificity of this method of investigation, privacy will not be the most prominent
determinant of behavioral intention in the consumer decision-making process.
H1a. Privacy is not the strongest determinant of behavioral intention.
While the previous hypothesis may be interesting for future research investigating
the importance of privacy for E-commerce acceptance, it is fairly straight-forward, and not
particularly meaningful for future privacy research. The main focus of this paper lies with the
privacy concern construct. It would be more meaningful to investigate the importance of
privacy in relation to security. As explicated in section 2.4 (privacy trade-offs), the financial
risk related to security provides a clearer causal relation between sharing personal
information and loss. We propose that security is therefore a stronger determinant of
behavioral intention than privacy. In case H1a is confirmed, and security is not the strongest
determinant of behavioral intention, we propose the following addition to H1a:
H1a\2. Privacy is a weaker determinant of behavioral intention than security
Accordingly this paper intends to investigate the adequacy of the privacy concern
construct in privacy investigations. This paper proposes that privacy, security and control
affect behavioral intention in distinct manners. A maximum likelihood factor analysis will be
conducted on the coefficients of the attributes in order to identify the structure among the
independent variables ‘privacy’ ‘security’ and ‘control’. Through this manner of investigation
21
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
we can discover if these attributes measure the same latent construct. If privacy, security
and control are subsets of the higher abstraction construct ‘privacy concern’, the variables
should influence behavioral intention in a similar manner (i.e. the coefficients for privacy,
security and control correlate lowly (-0.3 < r < 0.3) to the same latent factor(s)). Therefore, if
the factor analysis shows that the privacy coefficient does not relate to the same latent
construct as security or control, this would indicate that these variables do not affect
behavioral intention in a similar manner in the consumer decision-making process. We
propose:
H1b. The privacy coefficient and security coefficient do not correlate highly to the
same latent factor(s).
H1c. The privacy coefficient and control coefficient do not correlate highly to the same
latent factor(s).
The research of Tan et al. (2012) found the following effects: for consumers who
indicate high privacy concern ‘perceived usefulness’ is a stronger determinant of behavioral
intention while ‘perceived ease-of-use’ is a weaker determinant of behavioral intention, in
respect to consumers who indicate low privacy concern. In other words, consumers’
indicated degree of anxiety related to personal information disclosure is positively related to
the strength of the variable ‘perceived usefulness’s effect on behavioral intention and
negatively related to the strength of the variable ‘perceived ease-of-use’s effect on
behavioral intention. Support for this can be found in the study of Li and Unger (2012), who
found that for online services that use personalization technologies, i.e. presenting products
or offerings tailored to customers’ needs, customers who have fallen victim to an invasion of
privacy in the past considered the benefits that this technology brought to performance
were less important. As mentioned in section 2.3 – Privacy attitude, consumers with
negative privacy experiences are more likely to feel a higher degree of anxiety toward future
privacy-sensitive behavior.
However, as explicated in section 2.4 – Privacy trade-offs, the trade-offs discovered
by Tan et al. (2012) are not necessarily valid outside of the research context. One
explanation for this is the breadth of the privacy concern construct. This paper intends to reexamine the findings of Tan et al. (2012) in an E-commerce context, by investigating their
findings for the effect of the psychological component of privacy. Since the coefficient of
privacy reflects the strength of the anxiety related to consumers’ personal information
disclosure, and the coefficients of perceived usefulness indicates the relation between
perception of usefulness and behavioral intention, we propose the following hypothesis to
investigate the findings of Tan et al. (2012):
H1d: The privacy coefficient is positively related to the perceived usefulness
coefficient.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
Similarly to the findings of Featherman and Fuller (2003), Tan et al. (2012) state that
the effect of perceived ease-of-use on behavioral intention is lower (statistically
insignificant) for those who indicate high privacy concern in respect to those who indicate
low privacy concern. However, considering we assume the relation between privacy and
perceived usefulness to be positive, these findings are quite counter-intuitive. Due to
cognitive dissonance, it seems more likely that consumers who indicate a high degree of
anxiety toward privacy will either discount all perceived benefits of the technology in their
evaluations, in respect to consumers who indicate a low degree of anxiety toward privacy, or
will favor all benefits over privacy. If we consider the aforementioned findings to be
unrepresentative, than there is little confirmation regarding the direction of this trade-off.
Therefore we simply propose to go with our intuition and state that, similarly to perceived
usefulness, there will be a positive relation between the coefficients.
H1e: The privacy coefficient is positively related to the perceived ease-of-use
coefficient.
Lastly, we will investigate the privacy-price relationship. We propose that, due to
cognitive dissonance, the perceived benefit of a lower price plays a less important role for
consumers who show a high degree of anxiety toward a loss of personal information.
H1f: The privacy coefficient is positively related to the price coefficient.
3.5
Moderating variables
The moderating variables act as segmentation criteria for the respondent base. We
can use the respondent base’s heterogeneity in order to investigate if, and how, personal
factors may influence behavioral evaluations in a privacy context. This can be done by
analyzing the difference in the coefficients for the segments. This section will explicate the
moderating variables, and propose a set of hypotheses to help answer the research
question.
3.5.1 Privacy attitude
Traditionally, privacy research investigates the construct ‘privacy concern’ in order to
empirically investigate the abstract concept of privacy, and relate it to established constructs
such as behavioral intention. The construct typically includes an evaluative component. The
23
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
literature tends to express this through either the relation between ‘privacy awareness’ or
‘privacy knowledge’ and privacy concern (Smith et al., 2011), the relation between
‘presented information’ or ‘media exposure’ and privacy concern (Malhotra et al., 2004;
Pavlou et al., 2007), the relation between a former attitude or ‘privacy experience’ and
privacy concern (Malhotra et al., 2004; Phelps et al., 2001; Martin, 2012) or the relation
between perceptions regarding specific features such as a privacy seal or statement and
privacy concern (Belanger et al., 2002; Breward, 2007). Other works of literature that
investigate privacy concern tend to incorporate the evaluative component in the variable
itself. Literature that investigates privacy through a risk perspective for example generally
refers to privacy concern, or privacy risk, as the anxiety derived from an evaluation of
perceived privacy violations (Breward, 2007; Featherman and Pavlou, 2003; Im et al., 2008,
Tan et al., 2012, Zhou, 2012).
The evaluative component of the construct privacy concern reflects an attitude as
formed by beliefs and emotions regarding privacy. This attitude typically ranges from
completely neutral, i.e. no privacy concern, to completely negative, i.e. very high privacy
concern. Positive privacy attitudes are generally not considered, since privacy is considered
to be a resource of consumers that they can/have to expose in order to gain the benefits of
the product or service. Privacy concern and privacy attitude are similar in that they are both
expressions of favorability toward a specific attitudinal object as derived from emotions and
cognitive evaluations. But since the privacy concern construct includes privacy, security and
control aspects, this construct is not necessarily representative of consumers’ attitudes
toward privacy. In addition, the privacy concern construct generally only targets components
related to the anxieties and evaluations of privacy considerations (Smith et al., 1996;
Malhotra et al., 2004), but ignores the degree to which these anxieties and evaluations could
influence cognition and behavior as well as their resistance to change (attitude strength).
Particularly since privacy investigations are vulnerable to respondent bias, a measure of
attitude strength may lead to more predictive relations. Therefore we propose the variable
‘privacy attitude’, which relates only to the psychological component of privacy as mediated
by attitude strength. The privacy attitude construct also alleviates the necessity to
investigate the effect of privacy experiences as a separate variable, since privacy attitude
reflects the strength of the attitude which is considered to be the factor that distinguishes
the responses of consumers with privacy experiences from those without privacy
experiences.
The respondent base will be segmented in four groups based on their heterogeneity.
The groups reflect the degree of favorability respondents have towards privacy and the
strength of this attitude. Consumers are considered to fall in the following segments: weak
privacy attitude, strong privacy attitude, weak neutral attitude and strong neutral attitude.
Two or more of these segments may be combined based on the similarity of their decisionmaking process as exposed in the cluster analysis. For example, both consumers with a
weak negative attitude and a neutral attitude may evaluate the attitudinal object based on a
24
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
similar evaluation of situational cues when presented with an option to engage in privacysensitive behavior (Petty and Cacioppo, 1986).
The items which we measure the privacy attitude construct with have to be carefully
considered. In principal, attitude can be measured as a degree of favorability toward an
attitudinal object (Holland et al., 2002). But applying the same scale so that respondents can
evaluate privacy will likely lead to biased results (Malhotra et al., 2004). In order to discover
valid results, privacy attitude has to target latent privacy considerations. We adopt
constructs from the original scale for measuring privacy concern (Smith et al., 1996) in light
of scales constructed to investigate privacy concern regarding internet (Malhotra et al.,
2004) and E-commerce (Pavlou et al., 2007), which we consider in light of attitude literature
(Ajzen and Fishbein, 2000; Holland et al., 2002). Privacy attitude is measured through three
items that are proposed to capture the respondents’ degree of favorability toward the
conduction of privacy-sensitive information in E-commerce. Two items are affect-based
(measures of anxiety) and one measures favorability through external evaluation. Four items
to measure privacy attitude strength are adopted from attitude literature (Krosnick and
Smith, 1994; Pomerantz et al., 1995). Arguably, more items would increase the significance
of the construct, but a total of seven items was chosen in order to avoid biased results, due
to the length of the survey as well as common method variance. All items are constructed as
not to invoke social desirability bias or lead respondent considerations.
By testing the effect of privacy attitude on the relation between the privacy, control
and security attributes and behavioral intention, we propose a manner of investigation that
aims to discover discrepancies between the privacy concern construct and behavioral
intention. Due to the amount of segments and uncertainty regarding this construct, the
hypotheses related to privacy attitude are constructed solely to investigate if there is a
significant effect, rather than specifically test the direction of this effect. Assumptions
regarding the distribution of the effects of privacy attitude on the relations between the
independent variables and behavioral intention will be addressed in this section, and further
discussed in the section ‘Data collection and analysis’. Privacy attitude is designed to
measure a higher abstraction of privacy considerations than the privacy concern construct.
Privacy attitude relates to the latent perceptions regarding how privacy ought to be
conducted in an E-commerce context. This paper will investigate whether the variable
‘privacy attitude’ has a significant effect on the relation between the privacy attribute and
behavioral intention. The privacy attribute was constructed based on the items of privacy
concern in former research regarding the collection and distribution of personal information,
as well as relating to the passive dimension of privacy awareness. The privacy attribute is a
construct derived from the privacy concern construct designed to be more reflective of the
decision-making process, and targets the same latent considerations as the variable privacy
attitude. This inclines that privacy should follow a significant positive distribution along the
segments of privacy attitude, i.e. neutral respondents indicate low privacy coefficients, weak
negative respondents indicate higher privacy coefficients and strong negative respondents
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
indicate the highest coefficients. However, privacy attitude includes the aspect of attitude
strength. Therefore, by investigating whether there is a significant difference in the relations
between privacy and behavioral intention for the different segments of privacy attitude, we
can establish whether the strength of consumers’ privacy attitudes influences the
significance of privacy for explaining behavioral intention in the consumer decision-making
process. We propose:
H2a. Privacy has a significantly different effect on behavioral intention for consumers
who indicate different levels of privacy attitude.
When we investigate the effect of privacy attitude on the relation between security
and behavioral intention however, we expect different results. This paper assumes that
privacy and security affect behavioral in distinct manners. The latent considerations
regarding privacy are not considered to affect the importance of security. Therefore it is
considered unlikely that privacy attitude has a significant effect on the relation between
security and behavioral intention. We expect to confirm the null-hypothesis of the following
hypothesis:
H2b. Security has a significantly different effect on behavioral intention for consumers
who indicate different levels of privacy attitude.
We do expect a significant relation between privacy attitude and the relation
between control and behavioral intention. As explained in section 2.2 – Perceived behavioral
control, this relation might help explain the behavioral intention-behavior discrepancy. If
control is positively distributed among the segments, this would indicate that consumers
with a stronger (negative) privacy attitude require a greater deal of control over personal
information in order to justify engaging in behavior, in respect to consumers with a weaker
(negative) privacy attitude. We propose:
H2c. Control has a significantly different effect on behavioral intention for consumers
who indicate different levels of privacy attitude.
3.5.2 Involvement
The decision-making process of consumers who are involved with the attitudinal
object differs from that of consumers who are not involved (Petty and Cacioppo, 1986). The
behavioral decision is intrinsically more important to consumers who are involved with the
attitudinal object. Therefore the perceived psychological risk of making the wrong decision is
26
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
generally higher for these consumers (Dholakia, 2001). Involved consumers are more likely
to search and analyze all related information that is available to them. On the other hand,
consumers who are not involved will likely not care to make a carefully considered
evaluation of the attitudinal object (Petty et al., 1983). These consumers are considered to
base their intention for engaging in behavior on emotionally appealing stimuli. For example,
uninvolved consumers could decide not to use an E-commerce site because it has a
disclaimer it uses cookies, which they associate with a loss of privacy. While involved
consumers may care to find out what cookies are, how they work, and how they affect them,
and therefore decide on whether to engage in behavior based on an evaluation of their
perceived benefits and disadvantages. Given that privacy considerations presented in the
media are generally negative, this would incline that when consumers evaluate the decision
based only on information regarding the privacy implications of an E-commerce site, that the
intention to use this site will likely be higher for involved consumers than for uninvolved
consumers, ceteris paribus. As Akhter (2014) explains, involved consumers are more likely to
evaluate information on the attitudinal object that serves to mitigate their privacy concern.
This inclines that when involved consumers want to engage in behavior, they are more likely
to avoid cognitive dissonance by considering information that serves to mitigate privacy,
security and control considerations, in respect to uninvolved consumers.
On the other hand, consumers who are involved with the technology are more likely
to consider risk perceptions and their perceived probabilities to be more important than
consumers who are not involved (Venkatraman, 1989). Therefore, the anxiety associated
with privacy, security and control perceptions is likely to have a stronger effect on behavioral
intention for involved consumers than for uninvolved consumers (Pavlou et al., 2007). An
involved consumer should therefore be less likely to mitigate privacy considerations in light
of the perceived benefits, i.e. perceived usefulness, perceived ease-of-use and lower price,
than an uninvolved consumer.
Involvement is measured through three items, two related to the perceived intrinsic
importance of E-commerce, and one related to the willingness to search and analyze
information. Involvement will be measured before consumers are asked to evaluate a
decision, in order not to lead their answers (i.e. consumers might not give a valid score for
involvement if they first analyze the extent of the dimensions related to the behavioral
decision). The respondent base will be segmented in two groups based on their
heterogeneity. The segments will reflect if the consumers are relatively involved with online
shopping, i.e. indicate high involvement regarding E-commerce, or relatively uninvolved, i.e.
indicate low involvement regarding E-commerce.
Respondents are presented with information regarding privacy, security and control
that has a neutral tone-of-voice. No information regarding benefits and drawbacks is
available to respondents in the research. Therefore, respondents are considered to evaluate
the attributes based on how the attributes relate to their pre-existing attitudes toward
27
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
privacy, security and control. The attributes serve to mitigate their risk perceptions regarding
the dimensions of privacy concern, e.g. if consumers believe security is the most important
aspect, they are considered to weigh most importance on the security attribute since it
serves to mitigate their perceived security risks. Consumer evaluations of the attributes
reflect the weight they assign to their relative risk perceptions and perceived probabilities of
occurrence. In this line of reasoning, the privacy and security attributes ought to be stronger
determinants of behavioral intention for involved consumers than uninvolved consumers.
However, if this is not the case, it would infer that involved consumers are more likely to
discount their privacy and security considerations in the cognitive justification of using an Ecommerce site, relative to uninvolved consumers. It could also mean that uninvolved
consumers are more likely to indicate high privacy concern due to the evaluation of
emotionally appealing stimuli regarding the negative nature of privacy and security
implications. The object-specific attitudes that consumers form, and therefore the casespecific attitudes on which respondents base their stated behavioral intentions in privacy
research, are not very predictive of observed behavior for uninvolved consumers (Petty and
Cacioppo, 1986). Therefore, the finding that the privacy and security attributes are weaker
determinants of behavioral intention for involved than for uninvolved consumers might help
explain the discrepancy between stated intentions and observed behavior in privacy
research, i.e. if involved consumers are more likely to mitigate privacy and security
considerations when evaluating the behavioral decision and uninvolved consumers are
prone to state misrepresentative intentions to engage in behavior, than privacy concern
would not be a reliable predictor of behavior for either segment. We propose:
H3a. Privacy is a weaker (vs. stronger) determinant of behavioral intention for
consumers who indicate high (vs. low) involvement.
H3b. Security is a weaker (vs. stronger) determinant of behavioral intention for
consumers who indicate high (vs. low) involvement.
Consumers who are involved are more likely to consider risk perceptions as more
important and require cognitive justification of these perceived risks. We propose that
control is a stronger determinant of behavioral intention for involved consumers than for
uninvolved consumers, since control is considered to mitigate these risk perceptions.
H3c. Control is a stronger (vs. weaker) determinant of behavioral intention for
consumers who indicate high (vs. low) involvement.
This paper investigates the trade-offs consumers make between privacy concern and
the determinants of technology acceptance. While Tan et al. (2012) found significant
evidence for the direction of these trade-offs, we cannot assume that this direction is
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
representative for contexts other than social networking due to the role of involvement. As
aforementioned, the barriers for registering for a social network are relatively low.
Specifically in respect to a behavioral decision that is perceived to entail strong risk elements
for consumers, it is less likely that consumers will be highly involved and rely on cognitive
justification to make a decision. When respondents are not involved with the behavioral
decision it is more likely that they base their evaluation on situational cues or emotionally
appealing stimuli in their evaluations. (Petty et al., 1983). Therefore perceived usefulness
and perceived ease-of-use may not have the same effect on behavioral intention for
involved and uninvolved consumers in a privacy context. We propose that involved
consumers are more likely to discount the perceived benefits of the technology (such as its
perceived benefits in performing activities) when evaluating an attitudinal object, than
uninvolved consumers, who are considered to be more likely to discount their privacy
considerations in order to avoid cognitive dissonance.
H3d: Perceived usefulness is a weaker (vs. stronger) determinant of behavioral
intention for involved (vs. uninvolved) consumers.
H3e: Perceived ease-of-use is a weaker (vs. stronger) determinant of behavioral
intention for involved (vs. uninvolved) consumers.
3.5.2 Trust
In an organizational context, trust is defined as the willingness to accept vulnerability
with regards to the company (Mayers et al., 1995). The conceptualization proposed by
Mayers et al. (1995) indicates that trust consists of a belief that the company encompasses
the integrity, benevolence and ability to show appropriate behavior towards the consumer.
Consumers’ perceived intentions regarding the company’s conduction of personal
information are crucial for the variable trust. Trust indicates that consumers believe the
involved parties will show appropriate behavior instinctively and because of their own
initiative, rather than limiting themselves to solely adhering to the responsibilities proposed
in possible contracts. Furthermore, it also indicates a belief that companies are capable of
doing so. If, for example, consumers believe that the company is willing to protect privacysensitive information but is legally obliged to share this information with a third party, it is
unlikely that their privacy concerns will be mitigated.
Trust will be measured through three items that measure the perceived
trustworthiness of E-commerce companies. One is related to integrity, one to benevolence,
and one to ability. The items will be derived from Pavlou et al. (2007) due to the similarities
between the context and trust considerations of their study and this research. Similar to the
construct of privacy attitude it could be argued that more items would increase the
29
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
significance of the trust construct, but a total of three items was chosen in order to avoid
biased results due to the length of the survey. The trust segments will reflect whether
respondents show a relatively high or low degree of trust toward companies in E-commerce.
While the direct relations between trust and privacy concern are relatively wellestablished, the moderating effect of trust on the relation between privacy concern and
behavioral intention could benefit from further investigation (Smith et al., 2011). Pavlou et
al. (2007) explain that trust can affect privacy concern in two manners: Consumer privacy
concerns are likely to be lower for consumers who trust the company, since the signals send
by companies can mitigate these concerns before the consumer needs to decide whether to
engage in behavior. The other way that trust can affect privacy concern occurs when
consumers evaluate alternatives. The most trustworthy firm is perceived to be less inclined
to inappropriately handle private information, therefore privacy concerns are lower for
consumers who trust the firm. However, neither of these direct relations apply to this
research. The attributes are constructed so that they mitigate the related perceptions of
respondents, and the only stimuli that consumers can base their decision on are the specific
attributes that may mitigate related concerns. Secondly, respondents will not be presented
with specific company or brand names. Their decisions are proposed to reflect the
perceptions regarding E-commerce in general. Therefore consumers are not considered to
perceive a difference in trustworthiness among the alternatives. This inclines that trust can
only affect the manner in which the independent variables relate to behavioral intention.
Consider consumers who perceive privacy to be important, and who trust E-commerce
companies in general. While these consumers can feel a high degree of anxiety toward
providing personal information, it will still be less important in their behavioral evaluation of
the alternatives since they may believe that all of the alternatives will adequately handle
their personal information. Hence, we propose that their stated concerns are less significant
predictors of behavioral intention. Similar logic applies to security, i.e. security concerns can
be mitigated if consumers believe that companies can and want to protect security, and
control, i.e. the need for control over personal information can be mitigated if consumers
perceive that companies can and want to adequately handle personal information. We
propose:
H4a. Privacy is a weaker (vs. stronger) determinant of behavioral intention for
consumers who indicate high (vs. low) trust.
H4b. Security is a weaker (vs. stronger) determinant of behavioral intention for
consumers who indicate high (vs. low) trust.
H4c. Control is a weaker (vs. stronger) determinant of behavioral intention for
consumers who indicate high (vs. low) trust.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
4.
Data analysis
4.1
Pre-test
An informal pre-test was conducted on 12 respondents (some of which were
presumed to hold strong privacy attitudes, some of which were presumed to hold weak
privacy attitudes). Respondents were asked to evaluate if they considered the presented
information and items capable of being understood by other respondents in the actual
survey, without leading their answers. The aim of the pre-test was to find if the survey was
adequately designed and phrased, the presented attributes were on an equal footing in
terms of the benefits they provided, if the survey could lead respondent answers, and was
short enough for respondents to be able to consider all information. This pre-test led to a
few important discoveries. For example, the initial survey included information about a
hypothetical E-commerce company, which the pre-test showed was unnecessary and forced
respondents to evaluate too much information. In addition, when respondents read the
information about the attributes they were prone to forget the characteristics of the specific
attributes on the following page. Including a note that respondents could read the attribute
descriptions on the previous page was considered too cumbersome, so a short reminder of
their most important qualities needed to be included on the evaluation page. An important
consideration of the survey was that respondents should not be explicitly aware that the
survey investigated privacy until after they had rated the conjoint profiles. The pre-test
confirmed that the manner in which items were presented gave the impression that the
survey solely investigated E-commerce.
The main issue of the survey was its length. Despite the fact that all items were kept
as parsimonious as possible, all text was kept to a minimum and the entire survey was
designed to be as short and straight-forward as possible given the methods of investigation,
respondents were not eager to read and evaluate. Therefore the choice was made to keep
the items for the moderating possible as short as possible, and to exclude holdout profiles
that would otherwise have helped to measure the validity of the conjoint analysis.
4.2
Data collection
The final survey (see appendix A) was distributed online via several channels in the
Netherlands: Multiple social networking pages, university mailing lists and websites
specifically designed for student research. The survey was distributed to a minimum of 312
subjects (not including social network exposure). Unfortunately the response rate was
extraordinarily low. The decision was made to expand the reach of the survey to channels
31
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
outside of the Netherlands, for which an extra item in the survey regarding language was
created in order to keep the responses separate. This paper is not concerned with the
antecedents of privacy concern but rather the effects. Therefore, unless there are many
statistical differences between the respondent groups, this is assumed to not affect the
validity of the research. The survey was distributed to a minimum of 395 additional subjects
– students of Aarhus University. A total of 161 subjects filled in the survey, out of whom 69
completed the survey. After the exclusion of invalid responses, respondents who rated low
on the excluding question regarding online shopping experience and outliers, a total of 56
useable responses were gathered from the online collection. The decision was made to
disengage from online collection methods and conduct a segregate field study. An additional
85 questionnaires were distributed among subjects in the waiting room of Den Haag city hall
and among train passengers, which yielded a total of 78 completed responses, out of which
65 were useable. The analysis is performed on a total of 121 responses. With a drop-out rate
of 8.2% the field study proved to be a preferable method over online surveys, which yielded
a drop-out rate of 57.1%. The high online drop-out rate may be attributed to the average
time to complete the survey, which is a little over 7 minutes. It can be concluded from this
that researchers should not base their method of data gathering on the study of Klein et al
(2010).
Although there is little difference in online- and traditionally gathered conjoint analysis data,
Online conjoint analysis data is considered to be slightly more reliable than data from
traditional channels since respondents don’t feel observed (Klein et al., 2010). To account for
this, respondents were told they were not required to fill in their name, the surveyor sat at a
distance from respondents, and respondents were asked to insert their filled-in
questionnaire in a closed shoebox. Several respondents were told about the problems of
social desirability bias and biased results due to observation, and were asked if this played a
role in their responses, which each of them disconfirmed. These statements could of course
have been given due to social desirability bias, however, they lead us to believe that due to
the method of investigation there is no reason to believe that these biases played a
significantly larger role in the field study than in the online study.
4.3
Respondent base
Due to the agglomeration of samples there is a risk of inconsistencies in the
responses. This could lead to some problems for the investigation, since it may indicate that
respondents evaluate the profiles and items in distinct manners.
Table 1 presents the respondent base.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
Table 1: Demographic, collection and familiarity sample descriptives
Gender
Age
Male
57 %
(N=69)
Female
43 %
(N=52)
Channels
15-25
26-35
36-45
46-55
55-65
40.5 %
(N=49)
31.4%
(N=38)
15.7 %
(N=19)
8.3 %
(N=10)
4.1%
(N=5)
Research method
Netherlands
Denmark and
other
Online survey
Field survey
82.6 %
(N=100)
17.4 %
(N=21)
45.5 %
(N=55)
54.5 %
(N=66)
Familiarity - I have purchased products online in the past
Likert-scale ratings (1 and 2 are excluded)
3
6.6 %
(N=8)
4
7.4 %
(N=9)
5
14 %
(N=17)
6
17.4 %
(N=21)
7
54.5 %
(N=66)
Due to the problem with data collection, a series of tests needed to be performed in
order to assess the existence of inconsistencies in the database. For structure purposes the
following sections will go into further detail regarding the tests, while this section concerns
itself mostly with the outputs. We can investigate for differences by performing an
independent T-test on the variables of investigation. The independent T-test requires a few
assumptions to be met in order to yield significant results.
Outliers will distort the result of the T-test, so these are removed. The dependent
variables need to be approximately normally distributed. Upon initial visual inspection
(histograms), most variables appear to be approximately normally distributed. For the
variables that may have deviated from normal distribution in the histograms an additional Qplot was created. All variables appear to be normally distributed, see Appendix C.
The T-test is conducted for the mean differences between the responses of the
different collection methods. The results can be found in Appendix D.
Independent T-tests also require homogeneity of variances. SPSS automatically uses
Levene’s test in the independent T-test. Levene’s test measures equality of variances. Ergo, if
Levene’s test is significant, this indicates that there is a statistically significant difference in
the variance between groups. As can be seen in the results of the T-test, there are 7 items
for which Levene’s test is confirmed. This is not ideal, since it indicates that there was some
difference in the degree to which the respondent groups were conservative or liberal in their
evaluations. However, no conclusions can be drawn from this without interpreting the
33
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
results of the T-test. SPSS incorporates the Welch-Satterthwaite method in order to expose
differences in the variable means when there is no homogeneity of variances.
There is a significant difference in respondent means for conjoint profile 7 – which
presents respondents with the attributes of privacy, security and price. Respondents of the
online study and field study indicate means of 6.47 and 5.58 respectively. However, for the
other conjoint profiles the means were closely related and the T-test showed insignificant
results. We have to take into account that the sample sizes are relatively small, leading to a
higher chance for distorted results. While the results of this should be taken into account in
further examinations, they are proposed to not significantly distort the analysis.
There is a significant difference in respondent means for item T1 – E-commerce
companies understand the market they work in, where respondents of the online study and
field study indicate means of 5.49 and 4.64 respectively. However, there is no significant
difference in T2 and T3. We conduct a quick factor analysis and reliability analysis to see how
well the items are interrelated (these will be discussed more in depth in the following
sections of the data analysis), and we can come to the conclusion that the items of trust are
significantly interrelated. Hence the results for T1 are proposed to not significantly distort
the analysis.
The same applies to PS3 - I am interested in privacy-related information, and PS4 My attitude towards privacy represents my personal values. PS3 shows means of 5.07 and
4.29 for online and field respectively, while PS4 shows means of 5.45 and 4.45. We perform
the tests on the items of privacy strength (see section 4.7 – Reliability and cluster analysis) ,
and propose that these results may also be distorted due to sample size.
The independent T-test is conducted for the mean differences between the
responses of the different countries (language) of collection. The results can be found in
Appendix E.
There is a significant difference in respondent means for item T2 – Promises made by
E-commerce companies are likely to be reliable, where respondents from the Dutch data
collection and English data collection indicate means of 4.31 and 5.05 respectively. But there
is no significant difference in the results of T1 and T3.
There is a significant difference in respondent means for item PS2 – I am certain that
my attitude towards privacy is the right one, in respect to others, and PS4. Respondents
from the Dutch data collection and English data collection indicate means of 5.49 and 4.64
respectively. But there is no significant difference in the results of PS1 and PS3.
To conclude the respondent analysis: this paper considers that there is no reason to
believe that the difference in collection methods resulted in serious validity issues. There is a
chance that the variables ‘Trust’ and ‘Privacy strength’ may be affected by the difference in
collection methods, however, it is more likely that the difference in these variables can be
34
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
explained by the small sample size. Regardless, we need to be careful in the analysis of these
variables due to the possibility of heteroscedasticity.
4.4
Conjoint analysis
Table 2 presents the regression coefficients of the attributes as discovered from the
conjoint analysis. SPSS uses ordinary least squares to measure these coefficients based on
respondent profile preferences. Since the attributes only have two levels, 0 refers to the
exclusion of the software relating to the independent variable and 1 refers to the inclusion.
The coefficients are equal to the part-worth utilities of attribute level 1 for each attribute
(level 0 works as a dummy variable, and is therefore always equal to 0).
Table 2: Coefficients
B Coefficient
Estimate
pu
peou
privacy
control
security
price
,618
1,048
-,651
,659
,870
,837
The utility function of the conjoint analysis is linear, and can be measured as the sum
of the coefficients multiplied by their attribute levels plus a constant. The utility function of
our model is:
U a  4.923  0.651 p  0.870s  0.659c  0.618u  1.048e  0.837d
Where
U a stands for the utility of a given alternative
p stands for the attribute related to privacy
s stands for the attribute related to security
c stands for the attribute related to control
u stands for the attribute related to perceived usefulness
e
stands for the attribute related to perceived ease-of-use
d
stands for the attribute related to price
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
We can measure the relative importance of each attribute in the consumer decisionmaking process by measuring the range of their part-worth utilities (in this case this is equal
to their coefficients) as a percentage of the sum of all part-worth utility ranges. The relative
importance of each attribute is displayed in table 3.
Table 3: Importance values
pu
peou
privacy
control
security
price
13,195
22,374
13,901
14,078
18,579
17,873
Table 3 indicates that the attributes related to perceived ease-of-use, security, price
and control are more important for explaining general behavioral intention than privacy.
However, these results may be attributed to the sample. Therefore, in order to investigate
hypothesis H1a, we conduct a T-test on the privacy coefficient using the coefficient of the
strongest determinant of behavioral intention (perceived ease-of-use) as a test value. While
the mean of the perceived ease-of-use coefficient is not necessarily representative of the
entire population (N), we consider the differences between the coefficients of our sample to
be reflective of the distance between means of the population. In order for the T-test to be
valid, we need to check its assumptions. After the exclusion of a few outliers (regarding only
the privacy coefficient. All above results have been re-examined. See appendix F for
coefficient distribution and T-test before exclusion), all coefficients were distributed
normally (see appendix H).
Table 4 shows the results of the T-test.
Table 4: T-test privacy coefficient - peou mean
Test Value = -1.048
t
df
Sig. (2-tailed)
Mean
Difference
95% Confidence Interval of the
Difference
Lower
privacy_L
3,053
120
,003
,39717
,1396
Upper
,6547
When we compare to an approximation of a normal distribution, the test value is
3.053 at 120 degrees of freedom with a p-value of .003, i.e. t(120)=3.043, p=.003. With p <
.05 it is clear that the results are significant. Under the assumption that the perceived easeof-use coefficient is representative of the population, or that the trade-offs between privacy
36
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
and perceived ease-of-use are representative of the population, we can state that privacy is
not the strongest determinant of behavioral intention. H1a is confirmed.
Since the security coefficient is higher than the privacy coefficient but not the
strongest determinant of behavioral intention, we perform the same test on the security
mean.
Table 5: T-test privacy coefficient security mean
Test Value = -0.87
t
df
Sig. (2-tailed)
Mean
95% Confidence Interval of the
Difference
Difference
Lower
privacy_L
1,685
120
,095
,21917
-,0384
Upper
,4767
With t(120)=1,685, p=.095, the results are (not surprisingly) not as statistically
significant as H1a. It could be argued that due to the size of the sample we may test the
results for a significance of p < 0.1, however, the size of the sample works as a double-edged
sword for that argumentation. Since the test value is not an absolute, but rather an
estimation based on the security score of the sample, we conclude that H1a\2 is not
confirmed.
4.5
Factor analysis
In order to investigate whether privacy, security and control relate to the same latent
construct (privacy concern), we conduct an exploratory factor analysis on the coefficients.
An orthogonal maximum likelihood factor analysis is generally considered to be the most
adequate measure of identifying latent constructs, as long as the factors, as well as the
variables, are independent of each other and normally distributed. Factor analysis measures
shared variance of the variables in order to relate them to a defined number of factors. We
use Kaiser’s rule of thumb to set the number of factors, which is to retain only the factors
that explain more than the average amount of the sample variance (eigenvalue > 1). In total
this leads us to investigate a total of 3 factors (see appendix I). Factor analysis works as a
dimension reduction technique. The downside of this is that through this method of
investigation, the factors only account for about 66% of the sample’s variance. This is
adequate, but not significantly much.
The factor analysis takes random points of the dataset’s variable scores and
accordingly maximizes squared loading variance across variables (varimax rotation) in
several (in our case 4) iterations. Table 6 presents the results.
37
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
Table 6: Rotated Factor Matrix
Factor
pu_L
peou_L
privacy_L
control_L
security_L
price_L
1
2
3
,082
,348
,120
,914
,370
-,040
-,049
,175
,989
-,027
-,030
,170
,356
,014
,076
,404
-,271
,556
a. Rotation converged in 4 iterations.
As is evident from table 6, privacy, security and control do not correlate highly onto
the same latent constructs. This is an indication that H1b and H1c may be disconfirmed.
However, one of the assumptions of factor analysis is sampling adequacy. If the sample size
is not adequately large enough, the results of the factor analysis may not be reliable. The
Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy indicates if our sample was
sufficiently large enough (see appendix I)
These results are not optimal. While the Bartlett’s Test of Sphericity confirms that
there are correlations among the sample that can be investigated with factory analysis, the
KMO measure of sampling adequacy is low. We generally want to see a sampling adequacy
that is, as a bare minimum, higher than 0.5. Our results are just below the bare minimum,
therefore we cannot state that the results of the factor analysis are fully reliable. Hence we
use an additional test to investigate H1b and H1c.
4.6
Bivariate correlation
In order to decide on the right method of investigation, we need to first understand
the relationship between privacy and the other coefficients. By plotting the coefficients
together (See appendix J), we can come to the conclusion that the relation between privacy
and price is somewhat linear, while the rest of the relations are non-linear. This inclines that,
technically, the privacy and price relation fulfills the assumptions of the Pearson ProductMoment correlation test. The Pearson correlation investigates the correlation between two
independent variables by measuring the averaged data of two variables. Accordingly, a line
of best fit is drawn between the variables, and the distance of the variables to this line is
measured. However, due to the nature of the variables, it would make little sense to
investigate linear relations. After all, we want to investigate similarities between the
strength of the variables for explaining behavioral intention. This would incline investigating
38
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
the position of the data at the extremes, or centre, of the distribution. The reason that there
may exist a linear relation between privacy and price is due to the price construct –
consumers are unlikely to indicate an inverse relation for price (i.e. indicate a favorable
attitude toward losing money), hence the data points of the coefficient or relatively
positively distributed from 0 to the top value (on average). Therefore all relations will be
investigated using the nonparametric version of the Pearson correlation: Spearman
correlation (see appendix K)
The results for the privacy-security relation show a correlation r=-.047 at a
significance of p=.612. Since p > 0.05, we can state that there is no statistically significant
relation between privacy and security.
The results for the privacy-control relation show a correlation r=.126 at a significance
of p=.167. There is no significant relation between privacy and control.
Considering that the factor analysis showed that privacy might not measure the same
latent construct as security and control, and since the variables are not significantly
correlated, H1b and H1c are confirmed.
The results for the privacy-perceived usefulness relation show a correlation r=-.074 at
a significance of p=.417. Since p > 0.05, this indicates that there is no significant relation
between privacy and perceived usefulness. H1d is not confirmed.
The results for the privacy-perceived ease-of-use relation show a correlation r=.207
at a significance of p=.023. Since p < 0.05, we can state that here is a significant positive
relation between privacy and perceived ease-of-use. The test shows that for an average
increase of 1 of the privacy coefficient the perceived ease-of-use coefficient increases by
.207. In order to interpret the results we have to take into account the inverse nature of the
privacy coefficient. On the negative scale, consumers who indicate that their information
anxiety is a more important deterrent of engaging in behavioral intention, i.e. feel a higher
degree of anxiety toward personal information conduction, consider the benefits related to
perceived ease-of-use to have a more negative effect on behavioral intention. In other
words, the perceived ease-of-use of the technology is less likely to persuade consumers who
indicate a high degree of privacy anxiety to desire engaging in behavior. H1e is confirmed.
The results for the privacy-price relation show a correlation r=.3 at a significance of
p=.001. Since p < 0.005, we can state that there is a clear significant positive relation
between privacy and price. H1f is confirmed.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
4.7
Reliability and cluster analysis
In order to segment the respondent base on their scores regarding the moderating
variables’ items, we need to first examine the internal consistency of the items. This is done
by examining the Cronbach’s alpha output of a reliability analysis (see appendix L) as well as
by conducting a principal components analysis (see appendix M). The results show that all of
the items adequately measure the construct of higher abstraction and would not improve by
the deletion of items (with the exception of INV1, however, since the added value of
exclusion is low and the corrected items-total correlation for this item is low we propose it is
better to include it).
Principal components analysis is different from factor analysis in that it analyzes all
variances, and is better suited for combining variables into subsets. Though in SPSS the
output is the same. We control for 4 factors (one per item construct). With a KMO of .714
and 65% of variance explained, the items relate perfectly to the latent constructs without
significantly correlating to each other (see appendix M).
Accordingly the construct item responses were clustered using the K-means method.
The K-means method classifies segments through a few iterations of determining cluster
centers (means) and assigning the items that are closest. K-means clustering is effective for
identifying a pre-determined amount of clusters. The cluster results, and their group
descriptives, can be found in Appendix N.
In order to determine the number of clusters for the Privacy Attitude construct, an
ANOVA (see following section) was run for both 3 and 4 clusters (See appendix O and P).
Naturally the values differed, but they only differed slightly. The same tests were confirmed.
This indicates that consumers with a weak and strong neutral attitude have a similar
decision-making process. Therefore we run the analysis on 3 clusters, namely consumers
with a relatively neutral -, weak negative - and strong negative privacy attitude.
4.8
ANOVA
The Analysis of Variance (ANOVA) is a statistical method which tests the variance
between different samples and the variance within different samples. The main advantage
of ANOVA is that it allows us to investigate more than two groups. Unlike the T-test, which
compares the mean against a T-distribution, ANOVA compares the mean and variance
against a F-distribution, i.e. the ratio of two Chi-square random variables. A case can be
made that you could run multiple T-tests on the data and compare the results, however, this
would significantly increase the chance that the null hypothesis is falsely rejected.
40
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
ANOVA relies on the same assumptions as a T-test, so we use Levene’s test to
investigate homogeneity of variance (see appendix O). Control indicates p<0.05, which
indicates heterogeneity of variance between groups. Table 8 shows the F-values and pvalues the variables with homogeneous variance, as well as the methods of investigation and
p-values for control.
Table 8: coefficient p-values
Coefficient
F-value
privacy_L
security_L
Sig.
,492
5,245
,613
,007
Method
control_L
Ward
,007
Brown-Forsythe
,023
We can conclude from table 8 that there is no significant difference between groups
who indicate different levels of privacy attitude on the privacy coefficient (p=.613). The
clusters designed to represent the difference in responses on the dimensions of privacy
attitude do not show significant differences in respondent evaluations of the attribute
designed to measure the importance of personal information anxiety for determining
behavioral intention. In other words, even when stated concerns regarding privacy are
mediated by the strength of respondents privacy attitude, these privacy considerations have
not been found to effect the importance of privacy in the consumer decision-making
process. This result is a little unexpected, and could indicate several things.
It could indicate there is a measurement issue in the cluster analysis. Therefore we
run a multiple regression on the privacy coefficient. One of the assumptions of multiple
regression is that data cannot show multicollinearity. Therefore, we once more run a
principal components analysis on the moderating variable items (see appendix M). The
factor scores are saved using the Anderson-Rubin method, since this method correlates
items strongly within factors.
The factor scores for privacy favorability and privacy attitude strength are measured
against the privacy coefficient (See appendix Q). The results yield an R2 of .003 and adjusted
R2 of -.014, and the results of the accompanying ANOVA table show a significance of p=.843.
This indicates that the factors, and by an extension of reasonable doubt the items, cannot
predict the privacy coefficient. H2a is disconfirmed. Although the model is unreliable and we
cannot make any conclusions out of it, it is interesting to note the factor related to privacy
attitude strength is statistically deemed to be a more reliable indicator of the privacy
coefficient (p=.582) than the factor related to privacy favorability (p=.85).
41
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
What is interesting about the results is the relation of privacy attitude to the security
and control coefficients. Table 8 shows a significant difference between groups for security
(F(2,118)= 5.245, p=.007). A Tukey post-hoc test revealed that security was a significantly
weaker determinant of behavioral intention for respondents with a neutral privacy attitude
(.4257±.16) than respondents with a weak negative privacy attitude (1.106±.16, p=.015) and
respondents with a strong negative attitude (1.039±.15, p=.015). There were no statistical
differences between respondents who indicated a weak- and strong negative privacy
attitude (p=.952). H2b is confirmed.
Since the assumption of homogeneity is violated for the control coefficient, and since
we have unequal sample sizes, we use the more robust Welch, Brown-Forsythe and Gabriel
test to analyze the results. Both the Welch and Brown-Forsythe methods indicate a
significant difference between groups for control, respectively F(2,74)=4,079, p=.021 and
F(2,109)=3.425, p=.036. A Gabriel post hoc test shows that control was a significantly
stronger determinant of behavioral intention for respondents with a strong negative attitude
(.882±.18) than consumers with a neutral attitude (.291±.13, p=.043). H2c is confirmed.
Interestingly enough, the results show that respondents who indicated strong,
negative privacy attitudes consider security and control to be more important determinants
of behavioral intention than consumers with neutral privacy attitudes. Whereas there is no
significant difference, or even an indication, that privacy plays a more important role in the
decision-making of these respondents. The privacy attitude construct was designed so that it
might give representations of consumers’ anxiety regarding personal information exposure
that would be more reliable indicators their observed behavior. However, rather than
confirm the hypotheses designed for this investigation, the analysis exposed something far
more valuable for answering the research question. Namely that measuring higher
abstractions of privacy considerations through survey items is unlikely to yield results that
relate to the consumer’s psychological component of privacy. While this paper makes a clear
distinction between privacy, security and control, the results of H2 lead us to believe that
consumers may evaluate items regarding privacy based solely on their attitudes toward
security and control.
4.9
Independent T-test
In order to examine H3 and H4, this section will conduct independent T-tests on the
coefficients for the involvement- and trust clusters. See appendix R and appendix S for
involvement and trust respectively.
When we investigate the involvement clusters, we can see from Levene’s test that
there is homogeneity of variances between the high- and low involvement segments for all
coefficients. Considering that the respondent groups are also evenly distributed (the high42
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
and low involvement groups have a population of 59 and 62 respectively), the test is fairly
reliable and straight-forward. From the results of the T-test we can state:
Respondents who indicate high involvement regarded privacy to be a significantly
weaker indicator of behavioral intention (-.292±.19) than respondents who indicate low
involvement (-.992±.16), with t(119)=2.761, p=.007. This indicates that the assumption of
this paper that highly involved consumers are more likely to discount their privacy
considerations in the cognitive justification of using an E-commerce site than consumers
who indicate low involvement, is justified. H1a is confirmed.
As for security, since p=.99>.05 it can be stated that there is no significant difference
between the means of the groups for security. Although H3b is disconfirmed, the results of
this are interesting. At a significance of p=.99 >.95 we can in fact conclude that the two
respondent groups are significantly equal in their evaluations regarding the importance of
security on behavioral intention.
Respondents who indicate high involvement regarded control to be a significantly
stronger indicator of behavioral intention (1.013±.12) than respondents who indicate low
involvement (-.327±.12), with t(119)=3.521, p=.001. This confirms that highly involved
consumers are more likely to require a greater degree of control over the privacy-sensitive
behavioral outcome than consumers who indicate low involvement. H3c is confirmed.
There is no significant difference between the groups for perceived usefulness
(p=.432). H3d is not confirmed.
At a statistical significance of 5% there is no significant difference between the
groups for perceived ease-of-use (p=.097). H3e is not confirmed.
Accordingly an independent T-test is conducted for trust. The results (see appendix S)
indicate that there is homogeneity between variance for all coefficients.
At a statistical significance of 5% there is no significant difference in the importance
of privacy for explaining behavioral intention between the groups (p=.053). Although
technically this would incline that H4a is not confirmed, we have to consider that cluster
populations are unevenly distributed between the groups of trust (N=88 for consumers who
indicate high trust, whereas N=33 for consumers who indicate low trust). This may have
somewhat inflated the p-values in the T-test. Due to the large amount of former research
that shows significant relations between trust and privacy concern (see section 2.6 – The
role of trust) and the consideration that p-value only barely crossed the threshold, this
relation is given the benefit of the doubt. Respondents who indicate high trust regarded
control to be a weaker determinant of behavioral intention (-.497 ±.15) than respondents
who indicate low trust (-1.061±.25), with t(119)=1.952, p=.053. This indicates that
43
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
respondents who indicate trust in the industry of E-commerce as a whole, rather than in
specific E-commerce companies that may use signals to enforce trust and alleviate privacy
anxieties, consider privacy to play a less important role in their evaluations than consumers
who indicate a lack of trust. H4a is confirmed.
Since the p-values for the security and control coefficient are .5 and .801 respectively,
we propose that there is no significant difference in the effects of security and control on
behavioral intention for respondents who indicate high or low concern. H4b and H4c are not
confirmed.
5.
Conclusion and discussion
This paper examined distortions in the relation between behavioral intention and
observed behavior in privacy investigations regarding a technological context, by
investigating how privacy concern can affect consumers’ behavioral intention to use an Ecommerce site. Several theories for why stated behavioral intentions may not be predictive
of behavior in privacy investigations were proposed and investigated. The theories were
investigated by conducting a traditional conjoint analysis on hypothetical E-commerce
alternatives, which varied only on several tangible attributes. The attributes were proposed
to address the most important considerations regarding acceptance of a privacy-sensitive
technology. Through this method of investigation, this paper discovered the trade-offs
between the importance of privacy, security, control, perceived usefulness, perceived easeof-use and price for explaining behavioral intention. These trade-offs were tested for several
variables proposed to influence behavioral intention.
A theory regarding the discrepancy between stated behavioral intentions and
behavior was that consumers may assign an unrepresentatively high importance to privacy
in privacy investigations. When consumers are forced to make a trade-off between their
privacy-related anxiety and the benefits of engaging in behavior, it was found that privacy
played a much smaller role in explaining behavioral intention than is commonly noted in
former research. In addition, it was discovered that respondents may base their evaluations
of items related to privacy in surveys on their security and control considerations, rather
than their privacy considerations. When Malhotra et al. (2004) proposed the IUICP as a more
adequate measurement of privacy concern, the underlying reasoning was that accurate
investigations of privacy required sophisticated methods. The findings of this paper affirm
that statement. However, based on the findings of H1 and H2, this paper goes as far to say
that perhaps privacy considerations should not be investigated by means of survey items at
all. A reason for the discrepancy between the effect of privacy on behavioral intentions in
44
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
research and consumer behavior regarding privacy-sensitive decisions could be that privacy
is too abstract of a concept for consumers to indicate their latent considerations on surveys.
In addition to survey bias, the measurement of the construct ‘privacy concern’ has
been found to be unreliable. The items that are used to measure privacy concern in
contemporary privacy research relate to the anxiety regarding personal information
exposure as well the anxiety regarding a financial loss and the need for perceived behavioral
control, which were found to not be related. In addition to examining these relations, this
paper found a possible explanation for why these constructs are commonly agglomerated in
former research. Namely that respondents who state to be concerned regarding privacy may
in fact be likely to be concerned regarding a possible financial loss related to privacy
implications, and may desire a degree of behavioral control in a privacy context. Particularly
the relation of respondents who indicate a strong negative attitude towards privacy and the
relatively higher need for a degree of behavioral control in relation to privacy neutral
respondents may explain the discrepancy between behavioral intention and observed
behavior. After all, the Theory of Planned Behavior shows that behavioral intentions are not
reliable indicators of behavior of consumers’ need for behavioral are not met (Ajzen, 1991).
However, these results are addressed with caution. After all, the privacy attitude construct
was not specifically designed to test for this.
In order to investigate the extent of the effect privacy concern can have on
behavioral intention, the paper investigated the trade-offs consumers make between the
technology’s ability to address their privacy considerations and the perceived benefits of
using the technology. It was found that respondents who consider privacy to be an
important determinant of engaging in behavior are more likely to discount the perceived
benefits in the effortlessness of using a technology alternative, as well as perceived
monetary gains, than consumers for who the anxiety toward privacy conduction has a
weaker effect on behavioral intention.
Another theory for the reason why former works of privacy show different or invalid
(in respect to observed behavior) results is the moderating role of involvement. Consumers
who are not (highly) involved with the product or service are more likely to be influenced by
emotionally appealing stimuli at the time of their behavioral evaluation. In respect to highly
involved consumers their stated intentions are less predictive of behavior (Petty and
Cacioppo, 1986). However, respondents who indicate low involvement were found to
consider privacy to be a significantly stronger determinant of behavioral intention than
consumers who indicate high involvement. This finding urges the need for future privacy
research to control for involvement in order to determine predictive behavioral intentions.
Oddly enough, both consumers who indicated high and low involvement were found to
consider security to play a similar role in their behavioral evaluations. Regardless of the
consumers’ tendency to base behavioral evaluations on a carefully considered trade-off
between the benefits and risks of the behavioral object, or to base behavioral evaluations on
45
The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
heuristics and emotionally appealing stimuli, security is found to play an equally important
role in the decision making process. This once again illustrates the point that the financial
risk associated with privacy implications is a defined construct that affects consumers in a
clear and distinct manner. In addition, consumers who indicate high involvement were found
to be more likely to require a sense of control over the privacy-sensitive behavioral outcome
than consumers who indicate low involvement. This reaffirms the theory that when
consumers perceive to have no control over the behavioral outcome, that their stated
intentions are more likely to be unreliable predictors of actual behavior.
Lastly we propose that trust may play a moderating role in the relation between
privacy perceptions and behavioral intentions. This finding adds to the privacy literature in
the sense that trust may not only influence privacy considerations or be affected by them. It
may also distort the relation between the stated anxiety toward personal information
exposure and behavioral intention if this relation is not controlled for trust, regardless of
perceived differences between companies and consumers likelihood to be influenced by
signals send by companies to mitigate this anxiety.
5.1
Recommendations for future research
Although this study is confirmatory in design, it incorporated a great deal of
exploratory elements and was somewhat hypothetical in its nature. Therefore future
research would benefit from (large scale) empirical validations of the discovered relations
between privacy and behavioral intention. Particularly the use of a discrete choice analysis
might lead to more meaningful results. A design can be constructed that further inspects the
relation between the (latent) higher abstraction of privacy considerations and stated
responses regarding privacy, security and control. But most importantly, future privacy
research would benefit from an empirical validation of the relation between privacy
perceptions, stated behavioral intentions and observed behavior. It is only with a sufficient
analysis of the relation between these construct that privacy research can conduct
meaningful investigations of the effect of privacy considerations.
In addition, this paper focused heavily on investigating the acceptance of established
technologies. Particularly with all the advancements of biometric data gathering methods
the anxiety regarding personal information exposure may play a more significant role in
novel technologies.
The evaluation of privacy-related information plays a large role in the establishment
of an individual’s anxiety regarding personal information conduction. It is unclear to what
extent this may influence privacy consideration, and by the role of privacy in behavioral
evaluations. Privacy research could benefit from research using framing effects in order to
establish to what extent information regarding privacy can influence respondents.
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The role of privacy in technology acceptance - the effect of privacy concern on intention to use E-commerce
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