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Journal of Consumer Marketing Privacy concern and online transactions: the impact of internet self-efficacy and internet involvement Syed H. Akhter,

Article information: To cite this document: Syed H. Akhter, (2014) "Privacy concern and online transactions: the impact of internet self-efficacy and internet involvement", Journal of Consumer Marketing, Vol. 31 Issue: 2, pp.118-125, https://doi.org/10.1108/JCM-06-2013-0606 Permanent link to this document: https://doi.org/10.1108/JCM-06-2013-0606

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Privacy concern and online transactions: the impact of internet self-efficacy and internet

involvement Syed H. Akhter

Marketing Department, Marquette University, Milwaukee, Wisconsin, USA

Abstract Purpose – This study aims to examine the effects of internet self-efficacy and internet involvement on privacy concern and the effect of privacy concern on the frequency of online transactions. The study also seeks to examine the direct effect of internet self-efficacy and internet involvement on the frequency of online transactions. Design/methodology/approach – A structural equation model was developed to test the hypotheses. The model was tested using the LISREL 8.8 software and both structural and measurement parameters were estimated. Findings – Findings suggest that internet self-efficacy and internet involvement affect privacy concern negatively; and privacy concern, in turn, affects frequency of online transactions negatively. Findings also indicate that the direct effect of internet self-efficacy and internet involvement on the frequency of online transactions is positive. All parameters are significant and in the hypothesized direction. Practical implications – Findings highlight the significance of internet self-efficacy and internet involvement in explaining both privacy concern and online transactions. The negative impact of privacy concern shows strategically why businesses should take measures to protect privacy of consumers and assure them that the information they provide is protected and secure. Originality/value – This research contributes to the debate on privacy concern and adds to the growing body of literature on the impact of privacy concern on online transactions. Both antecedents and consequence of privacy concern are examined in the study.

Keywords E-commerce, Internet involvement, Internet self-efficacy, Online transactions, Privacy concern

Paper type Research paper

An executive summary for managers and executive

readers can be found at the end of this issue.

The warning about the threat to individual privacy came very

early. In 1973, Horst Feistel observed that “computers now

constitute, or will soon constitute, a dangerous threat to

individual privacy” (Feistel, 1973, p. 15). Four decades later,

the threat to individual privacy persists and remains a key

concern of consumers. Privacy concern remains salient

because of the massive amount of personal information that

businesses collect. The concern is also heightened because

consumers generally lose control over the data they provide to

businesses. The issue of privacy becomes salient especially when

consumers conduct online transactions because of the

personal and financial information required. The release of

such information is viewed as risky by consumers because

they become vulnerable to firms’ potential opportunistic

behaviors (Milne and Gordon, 1993). The information that

businesses collect can be misused by both authorized and

unauthorized users, raising privacy concerns (Clarke, 1998;

Webster, 1998). In a recent TRUSTe survey, 89 percent of

US adults indicated that they worry about their online privacy

and 55 percent strongly agreed with the statement that they

avoid doing business with companies that they do not trust

would protect their privacy. What is also noteworthy about

the survey findings is that 40 percent of the respondents felt

that they mostly or totally understand how to protect their

online privacy (Bachman, 2013). In another recent survey,

online shopping was the most commonly cited activity linked

with privacy concern (Tode, 2013). Although privacy concern is considered a critical dimension

of online consumer behavior, a review of the literature

indicates that very few studies have examined its psychological

antecedents. Phelps et al. (2001), in the direct marketing

context, found that positive attitudes toward direct marketing

reduced consumers’ privacy concern, whereas greater desire

for information control increased it. In a more recent study,

Youn (2009) found no significant relation between internet

self-efficacy and the level of privacy concern. Our goal is to

add to this body of research by focusing on two psychological

antecedents of privacy concern, internet self-efficacy and

internet involvement. We also examine how these two

antecedents of privacy concern and privacy concern itself

impact the frequency of online transactions. In the remainder of the paper, first, we cover the saliency of

privacy concern and propose a conceptual mode; second, we

develop and present the hypotheses; third, we cover issues

related to sampling, measurement, statistical technique, and

findings; and fourth, we conclude with a discussion of

findings and implications.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing

31/2 (2014) 118 – 125

q Emerald Group Publishing Limited [ISSN 0736-3761]

[DOI 10.1108/JCM-06-2013-0606]

118

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Conceptual model

From a consumer’s perspective, the experience of conducting

online transactions is psychologically and procedurally

different from other internet-related activities such as

exchanging emails, sharing pictures, or reading newspapers.

One of the requirements for conducting online transactions is

that consumers need to share personal and financial

information with the firm, which raises concern about

privacy and the misuse of personal information (Biswas and

Biswas, 2004). While the release of personal and financial

data can compromise privacy, it is also up to the consumers to

decide how much and with whom they share the information

on the internet. Thus, they can control the type of

information they provide, how they provide them, and to

whom they provide them. Drawing from social psychology and consumer behavior

literature, the paper proposes that self-efficacy and

involvement are significant antecedents of privacy concern.

Self-efficacy is defined as beliefs in one’s capabilities to

execute certain tasks (Bandura, 1998) and involvement is

defined as the state of perceived relevance of an object to a

person (Zaichkowsky, 1985). Applying these well-established

definitions to the internet domain, internet self-efficacy is

being defined as beliefs in one’s ability to navigate the internet

and accomplish different tasks; and internet involvement is

being defined as the extent to which the internet is perceived

as personally relevant. In the proposed model, internet self-efficacy and internet

involvement are shown to impact privacy concern.

Furthermore, internet self-efficacy and internet involvement

have both a direct effect and an indirect effect through privacy

concern on the frequency of online transactions (see Figure 1).

The proposed model has theoretical and strategic

significance. Theoretically, the adoption of an

interdisciplinary approach provides a richer explanation of

online transactions. The model also adds to the literature by

explaining both the antecedents and consequence of privacy

concern. From a strategic perspective, the study of the

antecedents of privacy concern is meaningful because online

transactions are expected to play a key role in generating

revenues for firms. Furthermore, as firms continue to build

the internet infrastructure, it will be crucial for them to

understand how privacy concern impacts the frequency of

online transactions. Findings from this study will also be

useful to managers in designing interventions strategies,

which are the different options available to firms to facilitate

and manage online transactions. We present the hypotheses

next.

Hypotheses

Internet self-efficacy and privacy concern

The use-related capabilities that people possess and the beliefs

they have about their ability to use a product determine how

the product will be used for achieving different goals. Bandura

(1997) suggests that as people’s lives revolve around achieving

desired goals, self-efficacy becomes the very core of human

life. Self-efficacy relates to the beliefs that people hold about

their “capabilities to organize and execute the courses of

action required to produce given levels of attainments”

(Bandura, 1998, p. 624). It is the perceived behavioral control

over the performance of the behavior itself that is associated

with self-efficacy (Ajzen, 2002). The use of a high-technology medium such as the internet

requires knowledge and skills. Research shows that people

with higher self-efficacy are more confident about their ability

to achieve different goals on the internet including the

management of privacy. They also view themselves as more

competent in solving problems that can arise when using the

internet. In contrast, people lacking use-related capabilities

may not feel confident in managing private information.

Venkatesh (2000) and Agarwal et al. (2000) found that computer self-efficacy increased the perceived ease of use.

People with higher levels of self-efficacy, for example, were

able to perform a task more easily and efficiently than those

with lower levels of self-efficacy. Although privacy concern is widespread, research shows

that people with greater ability are less concerned about the

security of information. Han and Maclaurin (2002) found

privacy fears to be the greatest among those who are less savvy

with technology. As self-efficacy increases people exhibit more

self-confidence in their ability to use the internet safely and

securely. They will know what information to provide and to

whom and how to provide the information, thus alleviating

privacy concern. Therefore, the following is proposed:

H1. As internet self-efficacy increases, privacy concern in using the internet will decrease.

Internet self-efficacy and online transactions

Existing research shows that self-efficacy influences both

attitudes and behaviors in different situations (Maddux et al., 1986). Self-efficacy leads to a judgment of competency about

completing a specific task. It also enhances the conviction to

mobilize cognitive resources to successfully execute a specified

task (Stajkovic and Luthans, 1998). For example, people with

higher levels of self-efficacy show greater liking for word

processors and personal computers and also higher intention

to use them (Hill et al., 1985). They also demonstrate greater willingness to use the internet for online transactions. As an

individual difference variable, self-efficacy captures

individual’s responses to challenges (Mukhopahhyay and

Johar, 2005). When beliefs about self-efficacy are

strengthened, commitment to search for suitable alternatives

for achieving goals increases (Locke and Latham, 2002). As using the internet for conducting online transactions can

be challenging, people with higher self-efficacy will tend to

experiment more and learn more. Giovannelli (2003) found

that experts are more reflective and achieve more expertise by

spending more time on a task. The perception of efficacy

encourages people to spend more effort in learning new things

(Bandura, 1997). On the other hand, feelings of low self-

Figure 1 Impact of internet self-efficacy and internet involvement on online transactions

Privacy concern and online transactions

Syed H. Akhter

Journal of Consumer Marketing

Volume 31 · Number 2 · 2014 · 118 – 125

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efficacy lead people to stay with the familiar even when a new

alternative is acknowledged as a better option (Selzer, 1983).

In a survey, 20 percent of the respondents indicated that they did not try out the internet due to lack of confidence or skills

(McIntosh, 2002). Self-efficacy is also found to increase the perceived ease of use (Venkatesh, 2000; Agarwal et al., 2000) and comfort level in handling the internet to achieve different

tasks. The behavioral link between self-efficacy and online transactions rests on the thesis that the perceived feeling of

competency will increase the frequency of online transactions. Therefore, the following is proposed:

H2. As internet self-efficacy increases, frequency of online transactions will increase.

Internet involvement and privacy concern

The construct of involvement has played a major role in explaining consumer behavior, since Sherif and Cantril

(1947) and Sherif (1980) hypothesized that involvement

occurs when an object or an issue is related to the unique cluster of attitudes and values that comprise a person’s ego.

Extending this view to marketing, Day (1970, p. 45) defined involvement as “the general level of interest in the object or

the centrality of the object to the person’s ego structure.”

Zaichkowsky (1985, p. 342), likewise, defined involvement as “a person’s perceived relevance of the object based on

inherent needs, values, and interests.” These definitions presume a subject-object relationship in which the subject

(person) gets involved with an object (product) because of its

relevance, importance, and centrality. As an internal state of arousal (Andrews et al., 1990),

involvement would induce the use of the internet and people would find themselves using the internet for different

purposes and with different frequencies. This can give rise to both situational and enduring relationships, a state in

which the person spends considerable amount of time

thinking about the product and interacting with it. Cass (2000) notes that as consumers interact with the product and

think about it because of the special place it occupies in their lives, they develop a cognitive structure related to the product,

which enhances the learning about the functionality and

requirements of the product. Park and Byeong-Joon (2003) show that with utilitarian products, products used for solving

problems, increasing involvement results in increasing product attribute knowledge.

In the case of a utilitarian product that can be used for different purposes, such as the internet, an increase in the

level of involvement can be expected to change the cognitive

structure resulting in enhanced knowledge about the internet. Knowledge about the internet will also increase the

confidence level and enable people to better manage the release of personal data on the internet. People will know

which organizations they can provide information to and with

whom they can interact and conduct business safely. Therefore, the following is proposed:

H3. As internet involvement increases, privacy concern in using the internet will decrease.

Internet involvement and online transactions

Involvement has both a cognitive and a behavioral component. At the cognitive level, the product is related to

consumers’ centrally held values (Arora, 1982) and is

reflected in the importance and relevance of the product to

consumers (Houston and Rothschild, 1978; Lastovicka and

Gardner, 1979). At the behavioral level, product involvement manifests itself in a number of observable physical

interactions. For example, in the case of cars, consumers

with higher levels of involvement may engage in different types of behavior such as performing their own repairs and

maintenance, correcting nicks and scratches, and taking cars

on pleasure drives (Bloch, 1981). Research shows that product involvement also influences information search

(Jacoby et al., 1978; Clarke and Belk, 1979), opinion leadership (Summers, 1970; Corey, 1971), decision making (Houston and Rothschild, 1978), and brand loyalty (Jacoby,

1971). Furthermore, as involvement increases, consumers are

also more likely to devote more time to a product (Bloch and Richins, 1983) and learn about its different uses. Yang (2012)

found that Facebookers’ involvement impacted their

purchasing intentions. And Bosnjak et al. (2007) found consumer affective involvement to be a significant

determinant of online shopping. Thus, it is being

hypothesized that as the level of involvement with the internet increases, consumers will tend to learn more about

the internet and use it for conducting online transactions. The

following is proposed:

H4. As internet involvement increases, frequency of online transactions will increase.

Privacy concern and online transactions

Concerns about privacy and security of personal information

are pervasive because of the massive amount of personal information businesses collect to build databases for

developing marketing strategies (Berry, 1994; Culnan and

Armstrong, 1999). As the use of the internet for conducting transactions requires sharing personal information, the

protection and authorized use of such information have

become a critical concern among consumers. Consumers are frequently exposed to news about identity theft, spyware,

adware, spam, phising, and pharming, among other things.

From consumers’ perspective, privacy concern arises from not knowing how the personal information they provide will be

protected and used. Yankelovich Partners found that 79

percent of respondents leave the websites when asked to provide private information and 90 percent reported that

privacy was the most pressing concern (Phelps et al., 2001). Privacy concern has also been found to reduce the use of direct marketing (Milne and Boza, 1999) and the intensity of

catalog purchase behavior (Phelps et al., 2001). Privacy concern is thus hypothesized to inhibit people from using the internet for conducting online transactions. Therefore, the

following is proposed:

H5. As privacy concern increases, frequency of online transactions will decrease.

Method

LISREL 8.8 was used to test the hypothesized relations in the

proposed model. The data to test the hypotheses was

collected through a survey questionnaire mailed to 5,000 randomly selected internet subscribers in the Midwest region

of the US. The total number of responses received was 1,190

(see Table I for sample demographics – age, education, and

Privacy concern and online transactions

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income). A covariance matrix, which is recommended for the LISREL statistical procedure, was used to test the structural and measurement parameters (Joreskog and Sorbom, 1996). We discuss next the measurement of the three latent constructs (internet self-efficacy, internet involvement, and privacy concern) and one non-latent construct (the frequency of online transactions) used in the model.

Measurement Internet self-efficacy Underlying the concept of self-efficacy is the belief of perceived ability to successfully organize and execute a task. Bandura (2001) recommends that measures of self-efficacy should be worded to reflect beliefs associated with “can do” rather than “will do.” As self-efficacy is the belief that people have about their competency or ability to complete a task successfully, this belief may vary depending on the task. Therefore, Peterson and Arnn (2005) recommend that measures of self-efficacy should not be general but task specific. Bandura (2001) also notes that general purpose measures of self-efficacy may have no relevance to the “selected domain of functioning.” In view of these recommendations, internet self-efficacy in this study focused on beliefs about skills, ability, and confidence related specifically to the use of the internet.

In existing studies, measures of self-efficacy with regard to the use of the internet cover the ability to perform specific functions on the internet such as entering a web address, creating folders, and adding or removing bookmarks (Nahl, 1996; Nahl and Meer, 1997). Ellen et al. (1991) measured self-efficacy by asking subjects about their perception of the

difficulty of using a computer program versus using the paper

and pencil method. Other measures cover the ability to

navigate the internet or close a browser (Joo et al., 2000). In this paper, beliefs reflecting three key elements (skill,

ability, and confidence) were combined to measure internet

self-efficacy (Tyson and Snyder, 1999; Easten and LaRose,

2000; Ma and Liu, 2005). The three items used to measure internet self-efficacy were worded as follows: I do not have the

necessary skills to fully use the internet (item reverse coded), I

do not have the necessary ability to fully use the internet (item

reverse coded), and I am confident that I can solve any

problems in using the internet. Respondents were asked to

indicate their level of agreement with these statements on a

Likert scale, ranging from 1 to 7, with 1 indicating strongly

disagree and 7 strongly agree. These three items also

correspond to Ajzen’s (2002, p. 676) suggestion that items

that load highly on self-efficacy deal with the “ease or

difficulty of performing a behavior, with people’s confidence

that they can perform it if they want to do so.”

Internet involvement Product involvement manifests itself as situational or

enduring (Rothschild, 1975; Houston and Rothschild,

1978). Situational involvement results from specific product

attributes and enduring involvement from long-term interest

of consumers in the product and is a function of individual

differences (Houston and Rothschild, 1978). In this study, the

shortened version of the involvement scale was used (Zaichkowsky, 1994). The scale captures both situational

and enduring involvement (Ram and Jung, 1994). A seven-

point semantic difference scale was used to obtain responses.

Respondents indicated whether the internet was:

unimportant-important, boring-interesting, irrelevant-

relevant, unexciting-exciting, meaningless-meaningful,

unappealing-appealing, ordinary-fascinating, worthless-

priceless, uninvolving-involving, and unnecessary-necessary.

Privacy concern A quick and convenient way to measure privacy concern

would be to directly ask consumers how concerned they are

about the ways companies use the data about them (Phelps

et al., 2001). This measure would be expected to summarize the concern that people have about privacy. However, this

measure was not considered adequate for our proposed

model, as privacy concern involves both the medium and the

person. Privacy concern was thus measured by three

indicators. Respondents were asked to indicate their level of

agreement on a Likert scale, ranging from 1 to 7, with 1

indicating strongly disagree and 7 strongly agree. The following three items were used: Privacy is protected on the

internet (item reverse coded), internet is secure for

confidential information (item reverse coded), and I am

concerned about my privacy on the internet.

Online transactions Online transaction is the behavioral dimension of the proposed model. Three types of online transactions were

studied: buying, banking, and investing online. Respondents

were asked to indicate the frequency of use of these three

activities, with frequency ranging from 0, not at all, to 7, very

frequently. As the data was collected via a survey

questionnaire, one of the concerns with self-reported

behavior relate to the accuracy of the information provided

by respondents. However, existing research shows a high

Table I Sample demographics

%

Age Under 19 8.0

20 to 34 14.1

35 to 54 51.9

55 to 64 23.4

65 to 84 2.5

Over 85 0.1

Education Less than 9th grade 0.8

9th to 12th grade 1.7

High school 15.9

Some college 30.2

Associate degree 10.9

Bachelor’s degree 23.4

Graduate or professional 17.2

Income Under $14,999 4.9

$15,000-$24,999 8.1

$25,000-$34,999 8.7

$35,000-$49,999 21.5

$50,000-$74,999 27.2

$75,000-$99,999 13.1

$100,000-$149,999 11.4

$150,000-$199,999 3.4

More than $200,000 1.7

Privacy concern and online transactions

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correlation between self-reports of computer use and the

electronic log data (Dean et al., 1998). The close

correspondence between these two types of data indicates

that self-reported data is reflective of actual use and,

therefore, can be used as a measure of the use of the

internet for transactional purposes.

Findings Model fit For the proposed model, the x

2 value of 844 with 146 degrees

of freedom was significant with p ¼ 0.00. However, as x 2

is

influenced by sample size, where for large samples even trivial

discrepancies become significant, it is recommended that

other fit indices be examined to judge the fit of the model

(Klem, 2000). These fit indices and their values are as

follows: the Goodness of Fit Index (0.92), Adjusted Goodness

of Fit Index (0.90), Comparative Fit Index (0.97), Non-

Normed Fit Index (0.97), and Root Mean Square Error of

Approximation (0.068). Based on the values of the above

indices, the overall fit of the model can be considered

satisfactory.

Measurement parameters Three latent constructs were used to explain the frequency of

online transactions. One indicator of each latent construct

was set to 1 to ensure that the construct was measured on the

same measurement scale as the corresponding indicator

(Hayduk, 1988). Reliabilities for the three constructs were as

follows: internet self-efficacy (0.77), internet involvement

(0.95), and privacy concern (0.60). While the reliabilities of

privacy concern and internet self-efficacy are in the acceptable

range, the reliability of internet involvement is in the high

range. Convergence validity for each construct was also

established. Average variance extracted (AVE) of 0.5 or higher

is considered as indicating adequate convergence. The AVE

was as follows: internet self-efficacy (0.55), internet

involvement (0.66) and privacy concern (0.54). To establish

discriminant validity, AVE should be greater than the squared

correlation between constructs. The AVE for each construct

was higher than the squared correlation estimate (Hair et al.,

2006).

Structural parameters The coefficient estimates for the various paths and the

associated t-values support all five hypotheses (see Table II).

All structural coefficients are significant and in the

hypothesized directions. The effect of internet self-efficacy

on privacy concern (j1 on h1, 20.17) is negative and

significant; and the direct effect of internet self-efficacy on the

frequency of online transactions (j1 on h2, 0.22) is positive

and significant, as predicted. The effects of internet

involvement on privacy concern (j2 on h1, 20.27) is

negative and significant; and the direct effect of internet

involvement on the frequency of online transactions (j2 on h2,

0.22) is positive and significant, as predicted. The effect of

privacy concern on the frequency of online transactions (h1 on h2, 20.24) is negative and significant, as predicted (see

Table II for parameter values and fit indices). As expected,

internet self-efficacy and internet involvement are correlated

and the correlation is positive and significant.

Conclusions

Theoretical implications

The proposed model adds to the literature by delineating both

the antecedents and consequence of privacy concern.

Specifically, findings suggest that internet self-efficacy and

internet involvement have a negative impact on privacy

concern. And privacy concern has a negative impact on the

frequency of online transactions. Furthermore, internet self-

efficacy and internet involvement have a positive impact on

the frequency of online transactions. Findings of this study

concur with Bandura (1997, 1998), Venkatesh (2000),

Zaichkowsky (1985) and Park and Byeong-Joon (2003) that

self-efficacy and involvement explain product usage. This

study also adds to the literature by showing that internet-self

efficacy and internet involvement have an impact on privacy

concern. In contrast to Youn’s (2009) finding of no significant impact

of privacy self-efficacy on the level of privacy concern, this

study found a significant and negative impact of internet self-

efficacy on privacy concern. The difference in findings can be

attributed to the conceptualization of the constructs. Youn’s

(2009) focus was on privacy self-efficacy and thus tapped into

the peoples’ confidence in protecting their privacy from e-

marketers’ information practices. Furthermore, the level of

privacy concern, measured by a single item, reflected concern

about the ways companies collect and use personal

information on the internet. Our conceptualization of these

two constructs was broader. Internet self-efficacy dealt with

skills, ability, and confidence in the general use of the internet,

not for a specific purpose such as protecting privacy. The

underlying assumption was that people with high internet self-

efficacy, besides being able to effectively navigate the internet,

would know how to protect their privacy. Similarly, privacy

concern was conceptualized as reflecting concern about

privacy on the internet and protection and security of

information on the internet. Both conceptualizations, thus,

attempt to capture a more comprehensive interpretation of

the two constructs. Internet self-efficacy was found to play a role in influencing

the frequency of online transactions because of the complexity

of the internet technology. The human-technology interface

brings together, on the one hand, the cognitive dimensions of

the user and, on the other hand, the functional features of the

internet. On the user side is the belief in the ability to

successfully execute different tasks on the internet and on the

technology side is the potential to render different types of

services. The user has to feel comfortable in using the internet

and this feeling of comfort is enhanced with internet

involvement. There is thus a significant correlation between

internet self-efficacy and internet involvement.

Strategic implications

Revelations about breaches of personal data at businesses

come along fairly regularly. Not long ago, data breaches at

companies such as Sony, JP Morgan Chase, Best Buy, Target

and 17 others, led Marc Rotenberg of Electronic Privacy

Information Center to comment that the “recent spate of

security breaches is off the charts” (Forden, 2011). And more

recently, loss of personal data such as user names, credit and

debit card numbers and other personal information at firms

such as 7-Eleven, Visa, Carrefour, J.C. Penney, Jet Blue and

others resulted in loss of more than hundreds of millions

Privacy concern and online transactions

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(Booton, 2013). These high-profile breaches have heightened

consumers’ awareness of the vulnerability of personal

information stored on data servers at businesses.

Consumers, therefore, remain justifiably concerned about

privacy because they do not know how data on them will be

used and by whom. When personal data are compromised,

the psychological and financial cost can be severe. Scott McNeally of Sun Microsystems famously stated that

consumers have zero privacy and they should get over it. While

his comment did not go over well with privacy watchdogs, it

did capture in many ways the reality of today’s business. Every

time a transaction occurs, data are exchanged. Businesses know

what was bought, what price was paid, how much was bought,

where it was bought, when it was bought, and, through credit

card information and home address, by whom. At a

fundamental level, information such as these constitutes the

backbone of data-based marketing and e-commerce. Not

surprisingly, it also constitutes a bone of contention between

businesses and consumers. Businesses need data to understand

their consumers and tailor their offerings, but consumers feel

that they need to protect their privacy so as not to become a

victim of cybercrime. In surveys and ordinary conversations,

consumers continue to identify privacy as one of their major

concerns when making online purchases. Privacy concern

remains at the forefront for many consumers and is reflected in

their hesitation to make online purchases. Findings from this study show the negative impact of privacy

concern on online transactions. The findings also show the

positive impact of internet self-efficacy and internet

involvement on online transactions. These findings have

strategic implications as they touch on marketing strategies

and consumers’ perceptions and behaviors. In recent years,

businesses have made significant investments in developing the

internet infrastructure to manage online transactions.

However, they need to recognize that the return on this

investment would depend on how willing consumers are to

conduct online transactions. Latest data show that e-commerce

sales account for only about 5.8 percent of total sales in the US

(US Department of Commerce, 2013). Businesses would

therefore need to invest in creating a digital environment that

generates trust and facilitates the completion of steps involved

in conducting online transactions. Furthermore, they should

also create a digital environment that promotes confidence

among users and encourages involvement. Thus, from the

firm’s perspective, engendering trust in online activities should

become an integral component of e-business strategy (Urban

et al., 2000). The findings also have implications for managing

consumers’ web experience. There is significant variation in

the frequency with which consumers buy online, some leaning

more towards conducting online transactions while others

completely abstaining from it. Many consumers shy away

from making an online purchase because of the challenges

they face in navigating the internet. These issues can be

addressed by enhancing internet self-efficacy and internet

involvement and lowering privacy concern. Businesses will

need to develop appropriate strategies to alleviate privacy

concerns and motivate the use of the internet for conducting

online transactions. To address privacy concerns, firms would

need to publicize the measures they have adopted to ensure

privacy and security of personal data. Putting privacy

safeguards in place and addressing privacy concerns will

improve web usage (Beck, 1998; Kargaonkar and Wolin,

1999; Caudill and Murphy, 2000). A customer centric

approach that is transparent in addressing privacy concerns

would benefit firms and consumers.

Research directions

This research extends our understanding of the antecedents

and consequence of privacy concern. However, there are

several issues that need to be addressed to further improve our

understanding of privacy. First, the concept of privacy used in

this paper did not make a distinction between the types of

information that some consumers might consider private that

others may not. This is an important distinction which can be

explored in future research. Second, the issue of relevancy of

privacy may not be an important consideration for some

consumers as they might think that in today’s information age

the notion of privacy is either outdated or irrelevant. This

issue may be demographically related and provides another

venue for exploring the notion of privacy further. Third, the

issue of trust and privacy may be closely related. Consumers

who trust a firm may not be as concerned about privacy as

those who do not trust the firm. This important linkage

should be explored further, as this has strategic implications

for firms.

Table II Parameters and goodness of fit statistics

Predicted sign From To Parameters estimate t-value

(2) Efficacy (j1) ! PrivCon (h1) 20.17 25.98

(1) Efficacy (j1) ! OnlineTran (h2) 0.22 6.96

(2) Involve (j1) ! PrivCon (h1) 20.27 24.10

(1) Involve (j1) ! OnlineTran (h2) 0.22 3.18

(2) PrivCon (h1) ! OnlineTran (h2) 20.24 26.63

Goodness-of-fit statistics Chi-square 844 ( p ¼ 0.00) degrees of freedom 146 Goodness-of-fit index 0.92

Adjusted goodness-of-fit index 0.90

Comparative fit index 0.97

Normed fit index 0.97

Root mean square error of approximation 0.068

Notes: Internet self-efficacy (Efficacy); Internet involvement (Involve); Privacy concern (PrivCon); Frequency of online transactions (OnlineTran)

Privacy concern and online transactions

Syed H. Akhter

Journal of Consumer Marketing

Volume 31 · Number 2 · 2014 · 118 – 125

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References

Agarwal, R., Sambamurthy, V. and Stair, R.M. (2000),

“The evolving relationship between general and specific

computer literacy: an empirical assessment”, Information

Systems Research, Vol. 11 No. 4, pp. 418-430. Ajzen, I. (2002), “Perceived behavioral control, self-efficacy,

locus of control, and the theory of planned behavior”,

Journal of Applied Social Psychology, Vol. 32 No. 4,

pp. 665-683. Andrews, J.A., Durvasula, S. and Akhter, S.H. (1990),

“A framework for conceptualizing and measuring the

involvement construct in advertising research”, Journal of

Advertising, Vol. 19 No. 4, pp. 27-40. Arora, R. (1982), “Validation of an S-O-R model for

situation, enduring, and response components of

involvement”, Journal of Marketing Research, Vol. 19,

pp. 505-516. Bachman, K. (2013), ADWEEK, January, p. 28. Bandura, A. (1997), Self-efficacy: The Exercise of Control, W.H.

Freeman & Company, New York, NY. Bandura, A. (1998), “Health promotion from the perspective

of social cognitive theory”, Psychology and Health, Vol. 13,

pp. 623-649. Bandura, A. (2001), “Guide for constructing self-efficacy

scales (revised)”, unpublished manuscript. Beck, R. (1998), “World wide web may mean worldwide

lawsuits”, Direct Marketing, Vol. 61 No. 3, pp. 60-65. Berry, J. (1994), “Database marketing: a potent new tool for

selling”, Business Week, Vol. 5, pp. 56-62. Biswas, D. and Biswas, A. (2004), “The diagnostic role of

signals in the context of perceived risks in online shopping:

do signals matter more on the web?”, Journal of Interactive

Marketing, Vol. 18 No. 3, pp. 30-45. Bloch, P.H. (1981), “An exploration into the scaling of

consumers involvement with a product class”, in Monroe,

K.B. (Ed.), Advances in Consumer Research, Association for

Consumer Research, Ann Arbor, MI, pp. 61-65. Bloch, P.H. and Richins, M.L. (1983), “A theoretical model

for the study of product importance perception”, Journal of

Marketing, Vol. 47, pp. 69-81. Booton, J. (2013), “Major corporations attacked in historic

hacking case”, Fox Business, 25 July. Bosnjak, M., Galesic, M. and Tuten, T. (2007), “Personality

determinants of online shopping: explaining online

purchase intentions using a hierarchical approach”,

Journal of Business Research, Vol. 60, pp. 597-605. Cass, A.O. (2000), “An assessment of consumers product,

purchase decision, advertising and consumption

involvement in fashion clothing”, Journal of Economic

Psychology, Vol. 21, pp. 545-576. Caudill, E.M. and Murphy, P.E. (2000), “Consumer online

privacy: legal and ethical issues”, Journal of Public Policy and

Marketing, Vol. 19 No. 1, pp. 7-19. Clarke, K. and Belk, R. (1979), “The effects of product

involvement and task definition on anticipated consumer

effort”, in Wilkie, W.L. (Ed.), Advances in Consumer

Research, Association for Consumer Research, Ann Arbor,

MI, pp. 313-318. Clarke, R.A. (1998), “Information technology and

dataveillance”, Communications of the ACM, Vol. 31 No. 5,

pp. 498-512.

Corey, L.G. (1971), “People who claim to be opinion leaders:

identifying their characteristics by self-report”, Journal of Marketing, Vol. 35, pp. 48-53.

Culnan, M.J. and Armstrong, P.K. (1999), “Information

privacy concern, procedural fairness, and impersonal trust:

an empirical investigation”, Organization Science, Vol. 10 No. 1, pp. 104-115.

Day, G.S. (1970), Buyer Attitudes and Brand Choice, Free Press, New York, NY.

Dean, F.P., Podd, J. and Henderson, R.D. (1998), “Relationship between self-report and log data estimates

of information system usage”, Computers in Human Behavior, Vol. 14, pp. 621-636.

Easten, M.S. and LaRose, R. (2000), “Internet self-efficacy

and the psychology of the digital divide”, Journal of Computer Mediated Communication, Vol. 6 No. 1.

Ellen, P.S., Bearden, W.O. and Sharma, S. (1991),

“Resistance to technological innovations: an examination

of the role of self-efficacy and performance satisfaction”,

Journal of the Academy of Marketing Science, Vol. 19 No. 4, pp. 297-307.

Feistel, H. (1973), “Cryptography and computer privacy”,

Scientific American, May. Forden, S. (2011), “Online privacy: can the US get its act

together”, Bloomberg Businessweek, 12 May. Giovannelli, M. (2003), “Relationship between reflective

disposition toward teaching and effective teaching”, Journal of Educational Research, Vol. 96 No. 5, pp. 293-309.

Hair, J.H., Black, W., Babin, B., Anderson, R. and Tatham,

R. (2006), Multivariate Data Analysis, 6th ed., Prentice Hall, Upper Saddle River, NJ.

Han, P. and Maclaurin, A. (2002), “Do consumers really care

about online privacy?”, Marketing Management, Vol. 11 No. 1, pp. 35-38.

Hayduk, L.A. (1988), Structural Equation Modeling with LISREL: Essentials and Advances, John Hopkins, Baltimore, MD.

Hill, T., Smith, N.D. and Mann, M. (1985),

“Communicating innovations: convincing computer

phobics to adopt innovative techniques”, in Lutz, R.

(Ed.), Advances in Consumer Research, Vol. 13, Association for Consumer Research, Provo, UT, pp. 419-422.

Houston, M.J. and Rothschild, M.L. (1978), “Conceptual

and methodological perspectives on involvement”, in Jain,

S.C. (Ed.), Educators’ Proceedings, American Marketing Association, Chicago, IL, pp. 184-187.

Jacoby, J. (1971), “A model of multi-brand loyalty”, Journal of Advertising Research, Vol. 11, pp. 25-31.

Jacoby, J.R., Chestnut, W. and Fischer, W.A. (1978),

“A behavioral process approach to information acquisition

in nondurable purchasing”, Journal of Marketing Research, Vol. 15, pp. 532-544.

Joo, Y., Bong, M. and Ha-Jeen, C. (2000), “Self-efficacy for

self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction”, Educational Technology Research and Development, Vol. 48 No. 2, pp. 5-17.

Joreskog, K. and Sorbom, D. (1996), LISREL 8: Users Reference Guide, SSI Scientific Software International, Chicago, IL.

Kargaonkar, P.A. and Wolin, L. (1999), “A multivariate

analysis of web usage”, Journal of Advertising Research, pp. 53-68, March-April.

Privacy concern and online transactions

Syed H. Akhter

Journal of Consumer Marketing

Volume 31 · Number 2 · 2014 · 118 – 125

124

D ow

nl oa

de d

by W

al de

n U

ni ve

rs it

y A

t 16

:4 3

04 A

ug us

t 20

18 (

P T

)

Klem, L. (2000), “Structural equation modeling”, in Grimm,

L.G. and Yarnold, P.R. (Eds), Reading and Understanding

More Multivariate Statistics, American Psychological

Association, Washington, DC, pp. 227-259. Lastovicka, J.L. and Gardner, D.M. (1979), “Components of

involvement”, in Maloney, J.C. and Silverman, B. (Eds),

Attitude Research Plays for High Stakes, American Marketing

Association, Chicago, IL, pp. 53-73. Locke, E.A. and Latham, G.P. (2002), “Building a practically

useful theory of goal setting and motivation”, American

Psychologist, Vol. 57, pp. 705-717. McIntosh, N. (2002), “The widening digital divide”,

The Guardian Unlimited, p. 3, July. Ma, Q. and Liu, L. (2005), “The role of internet self-efficacy

in the acceptance of web-based electronic medical records”,

Journal of Organizational and End User Computing, Vol. 17

No. 1, pp. 38-57. Maddux, J.E., Norton, L.W. and Stoltenberg, C.D. (1986),

“Self-efficacy expectancy, outcome expectancy and

outcome value: relative effects on behavioral intentions”,

Journal of Personality and Social Psychology, Vol. 51 No. 4,

pp. 783-789. Milne, G.R. and Boza, M. (1999), “Trust and concern in

consumers’ perceptions of marketing information

management practices”, Journal of Interactive Marketing,

Vol. 13 No. 1, pp. 5-24. Milne, G.R. and Gordon, M.E. (1993), “Direct mail privacy-

efficiency trade-offs within an implied social contract

framework”, Journal of Public Policy Marketing, Vol. 12

No. 2, pp. 206-215. Mukhopahhyay, A. and Johar, G.V. (2005), “Where there is a

will, is there a way? Effects of lay theories of self-control on

setting and keeping resolutions”, Journal of Consumer

Research, Vol. 31 No. 4, pp. 779-786. Nahl, D. (1996), “Affective monitoring of internet learners:

perceived self-efficacy and success”, Proceedings of the 59th

ASIS Annual Meeting, Vol. 33, pp. 100-109. Nahl, D. and Meer, M.P. (1997), “User-centered assessment

of two web browsers: errors, perceived self-efficacy, and

success”, Proceedings of the 60th ASIS Annual Meeting,

Vol. 34, pp. 89-97. Park, C. and Byeong-Joon, M. (2003), “The relationship

between product involvement and product knowledge:

moderating roles of product type and product knowledge

type”, Psychology & Marketing, Vol. 20 No. 11, pp. 977-997. Peterson, T.O. and Arnn, R.B. (2005), “Self-efficacy: the

foundation of human performance”, Performance

Improvement Quarterly, Vol. 18 No. 2, pp. 5-18. Phelps, J.E., D’Souza, G. and Nowak, G.J. (2001),

“Antecedents and consequences of consumer privacy

concern: an empirical investigation”, Journal of Interactive

Marketing, Vol. 15 No. 4, pp. 2-17. Ram, S. and Jung, H-S. (1994), “Innovativeness in product

usage: a comparison of early adopters and early majority”,

Psychology & Marketing, Vol. 11 No. 1, pp. 57-67.

Rothschild, M.L. (1975), “Involvement as a determinant of decision making styles”, in Mazze, E.M. (Ed.), Combined Proceedings, American Marketing Association, Chicago, IL, pp. 216-220.

Selzer, L.F. (1983), “Influencing the ‘shape’ of resistance: an experimental exploration of paradoxical directives and psychological resistance”, Basic and Applied Social Psychology, Vol. 4 No. 1, pp. 47-71.

Sherif, C.W. (1980), “Social values, attitudes and involvement of the self ”, in Page, M. (Ed.), Nebraska Symposium on Motivation, University of Nebraska Press, Lincoln, NE, pp. 1-64.

Sherif, M. and Cantril, H. (1947), The Psychology of Ego- involvement, Wiley, New York, NY.

Stajkovic, A.D. and Luthans, F. (1998), “Social cognitive theory and self-efficacy: going beyond traditional motivational and behavioral approaches”, Organizational Dynamics, Vol. 26 No. 4, pp. 62-74.

Summers, J.O. (1970), “The identity of women’s clothing fashion opinion leaders”, Journal of Marketing Research, Vol. 7, pp. 178-185.

Tode, C. (2013), “User concern over mobile privacy is growing”, Mobile Marketer, January 28.

Tyson, C.B. and Snyder, L.B. (1999), “The impact of direct mail video”, Public Relations Quarterly, pp. 28-32, Spring.

Urban, G.L., Sultan, F. and Qualls, W.J. (2000), “Placing trust at the center of your internet strategy”, MIT Sloan Management Review, Vol. 42 No. 1, pp. 39-48.

US Department of Commerce (2013), US Census Bureau News, Washington, DC, 15 August.

Venkatesh, V. (2000), “Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model”, Information Systems Research, Vol. 11 No. 4, pp. 342-365.

Webster, J. (1998), “Desktop videoconferencing: experiences of complete users, wary users, and non-users”, MIS Quarterly, Vol. 22 No. 3, pp. 257-286.

Yang, T. (2012), “The decision behavior of Facebook users”, The Journal of Computer Information System, Vol. 52 No. 3, pp. 50-59.

Youn, S. (2009), “Determinants of online privacy concern and its influence on privacy protection behaviors among young adolescents”, The Journal of Consumer Affairs, Vol. 43 No. 3, pp. 389-418.

Zaichkowsky, J.L. (1985), “Measuring involvement construct”, Journal of Consumer Research, Vol. 12, pp. 341-352.

Zaichkowsky, J.L. (1994), “Research notes: The personal involvement inventory: reduction, revision, and application to advertising”, Journal of Advertising, Vol. 23 No. 4, pp. 59-70.

Corresponding author

Syed H. Akhter can be contacted at: syed.akhter@ marquette.edu

Privacy concern and online transactions

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Journal of Consumer Marketing

Volume 31 · Number 2 · 2014 · 118 – 125

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