Article Critique

· Reference

· APA format

· Summary

· One paragraph (what the study was about primarily and what were the key findings)

· Write in your own words

· Do not just copy and paste the abstract

· Critique

· Minimum three paragraphs

· What was good?

· Subjects, study design etc.

· Shortcomings of the article

· Pick apart the article

· How you would take the study forward?

· Do not just go with their directions for future research

Student

Research Methods

23 February 18

Article critique #2

Vlaović, Zoran, et al. “Comfort Evaluation as the Example of Anthropotechnical Furniture Design.”

The purpose of the article was intended to portray the weight of the issue based on the comfort and discomfort of desk chairs. In order to find true answers, the authors conducted a study that tested multiple different chairs that had different size seats. The purpose of the study was to determine if size and composition of the seat would change comfort levels of individuals that spend large amounts of time sitting in them per day.

One thing I really loved about the article was how descriptive it was. It really looked deep into things that I never really thought about as an issue. I had never placed seat cushion size and shape as something that I would use as a determining factor to comfort over a long period of time, but I guess it does make a lot of sense. I felt like the diagrams really help a lot too in understanding all of the information really well! Overall, I thought the article was very informative and portrayed the information in a really clear and descriptive manner.

Student

Article Critique

Cao, H., Scudder, C., & Dickson, M. A. (20

1

7). Sustainability of Apparel Supply Chain in South

Africa. Clothing and Textiles Research Journal,35(2), 81-97. doi:10.1177/0887302×16688560

This report written by Cao, Scudder, and Dickson takes an in-depth look in the three categories that make up the influencing factors of the supply chain in relation to sustainability. They identify the the three pillars as economy, equity, and ecology. In this article the farmers, producers, vendors, retailers, and consumers, are looked at individually to see how the relationship between the three pillars will ultimately change the actions they take for sustainability. The Triple Top Line Model (TTL) is not to be confused with the Triple Bottom Line model (TBL). TBL is focused on reducing the negative effects that having a business strategy that is focused on economy or profits have on the social and environmental components of the industry. TTL wants to utilize the economical choices to positively effect society and environment. (McDonough & Braungart, 2002b). The issue with the current approach is that these problems are identified individually and not looked at in relation to each other (Gereffi & Frederick, 2010). Knowing all of this information the study proceeded into an interview with small farms, a commercial farm, cotton gins, yarn, textile, and apparel manufacturers, retailer, merchant, and a nonprofit organization. The interview questions asked about each component of the TTL and the practices the companies implement. The researchers created a point system that gave points for sustainable practices within economy, equity, and ecology combined in pairs (economy/economy, economy/ecology, economy/equity). This way they can rank how they connect each pillar together. The researchers concluded that most stages of the supply chain focus on profits and don’t correlate profits into equity and ecology. They especially found that smaller businesses and farmers had issues with adopting sustainable methods and ethical methods because of cost. To simplify their findings, economy drives everything and ecology is the weakest. In order to improve the supply chain economic growth needs to happen in order to save enough to invest into better practices. Considering this study was done in developing areas, there are multiple steps that need to be taken to add financial stability in order to proceed with solving equity and ecology (Cao, Scudder, Dickson, 2017).

The amount of information that was given in order to set the reader up for the data collection and conclusions was very helpful. It is evident that the authors of this journal are knowledgeable in the field and explored many sources prior to the study. The authors did a good job of simplifying conceptual ideas like the TBL and TTL models. It is a sign of high intelligence when complex areas of study, much like sustainability and the apparel supply chain, can be presented in a manner understandable by the public. In addition to their explanations they also included visuals to aid the understanding of specific areas their study is targeting and the data itself was put into tables for appropriate organization (adapted from McDonough & Braungart, 2002a, 2002b). In order to get a deeper look at the individuals they interviewed, a chart was provided with the positions the individuals held at the company and important notes about the roles in the supply chain (Cao, Scudder, Dickson, 2017). I thought including that information helped me trust the data and increased credibility of the results. They added a discussion with a nonprofit organization who told them about the current standing of the South African cotton industry. Including the main themes of that interview helped support their conclusion and gave a first hand source for information.

A few sub-categories of this journal in the discussion and results section got a little bit confusing. It was hard to sift through the data to find main themes and the use of codes as a percentage threw me off. The authors did a great job of simplifying background information, but they didn’t do as good of a job presenting data in a straight forward manner. I would have liked to see more visual interpretations of the results such as bar graphs, pie charts, or line graphs. I found the introduction and literacy review where a bit too long in comparison to their results and solution suggestions. By the end of the study the sections where significantly shorter and more repetitive, it seemed as though no distinct conclusion was made. If anything, the conclusion was supporting evidence for all of the background information they gave the reader at the start. The solution they came up with was essentially, to apply the TTL model into the business models of the supply chain. Basically the data they got from their interviews showed that the only solution is to approach the problem with a different mindset, which doesn’t directly help anything. They said that further research needs to be done on specific practices deemed sustainable and the longevity of those practices (Cao, Scudder, Dickson, 2017). After skimming through the whole journal multiple times I got further and further from the underlining purpose of the study. It would have been beneficial to address some questions I had such as; how does one apply a TTL model to a supply chain? How would they strengthen the economic pillar? What are some examples of successful use of the TTL model? And lastly, how much stronger does the economy pillar need to be in order to help the other two pillars?

This journal is a great source if you want to learn about sustainability models in relation to a supply chain in a developing country. It is a very informative journal that brings light to apparel production and retailing issues that are currently holding the global industry back from reaching a fully sustainable consumption cycle. Although the results didn’t conclude in a direct solution, it gave data to prove the theory that economy is a driving force and therefore sustainable practices must first address profitability (Cao, Scudder, Dickson, 2017). The authors took a broad issue and narrowed it down to the roots and gave the reader a starting place to make solutions.

References

Cao, H., Scudder, C., & Dickson, M. A. (2017). Sustainability of Apparel Supply Chain in South
Africa. Clothing and Textiles Research Journal,35(2), 81-97. doi:10.1177/0887302×16688560

McDonough, W., & Braungart, M. (2002a). Cradle to cradle: Remaking the way we make things. New York, NY: North Point Press.

McDonough, W., & Braungart, M. (2002b). Design for the triple top line: New tools for

sustainable commerce. Corporate Environmental Strategy, 9, 251–258.

1

Article Critique Rubric

Good
2 pts

Fair
1.5 pts

Poor
1 pts

Bad
0 pts

Summary

Good
The article is clearly but succinctly summarized – only the key points of the article are touched upon. The article summary takes up no more than one third of the total assignment.

Fair
The article is clearly summarized, but some sub points are addressed along with main points.

Poor
The article summary is unclear or overly detailed. Often well over half of the assignment is taken up by the summary.

No Effort

Critique

Good
Strengths and weaknesses that are central to the article are addressed. The discussion of strengths and weaknesses take up the majority of the assignment.

Fair
Strengths and weaknesses that are peripheral to the article are addressed. The discussion of strengths and weaknesses take up the majority of the assignment

Poor
Strengths and weaknesses are addressed peripherally, weakly, or not at all. The discussion of strengths and weaknesses take up only a small part of the assignment

Mechanics

Good
There are no grammatical errors or typos.

Fair
There are few grammatical errors or typos

Poor
There are many grammatical errors and/or typos

Format

Good
APA/MLA and page length requirements are met

Fair
APA/MLA and page length requirements are met

Poor
APA/MLA and page length requirements are not met.

Experiences of wake and light therapy in patients with depression: A qualitative study. International Journal of Mental Health Nursing

Kragh, M., Møller, D. N., Wihlborg, C. S., Martiny, K., Larsen, E. R., Videbech, P., & Lindhardt, T. (2017). Experiences of wake and light therapy in patients with depression: A qualitative study. International Journal Of Mental Health Nursing, 26(2), 170-180.

Summary

Researcher’s for this study designed a qualitative methodology approach. (Kragh et al. 2017) Thirteen participants diagnosed with moderate-to-severe depression were used. Individual interviews were done by a nurse who was previously known to the patients. This particular nurse happened to be the first author. Participants were asked to keep up with a diary, which the first author would read and use as notes to prompt individuals for more discussion later. Interviews would primary be done at the end of a 9-week period. These 17 individual interviews were conducted in a familiar place to the participants. A guide was devised to propose interview questions. Open and closed ended questions were used. Data was then recorded. (Kragh et al. 2017)

The data was collected and analyzed. (Figure 1) Several other researchers worked with the first author in this study by analyzing the data. The other researcher’s challenged the first author’s interpretation of the data. Together, the authors came up with an interpretation. Qualitative content analysis was used to evaluate the data. (Kragh et al. 2017) The study concluded that in general the participants benefited from the therapies. One main theme was identified, and that was that participants had an overall positive encounter with the therapy and intervention. (Kragh et al. 2017) Four sub themes were identified as well, which related to this positivism, however also reflected certain negative aspects. (Figure 2)

Critique

Depression is a major issue that many people are dealing with today. Depression is the world’s leading disability. (Kragh et al. 2017) While this may seem debilitating, there are many treatments to help those with this illness. Wake therapy is a sleeping treatment where patients are kept awake for a whole night and then the following day as well. Wake therapy is one of those treatments that has been proven to reduce symptoms in a matter of hours. Wake therapy tends to be paired with chronotherapeutic interventions which helps prevent depressive symptoms from returning. (Kragh et al. 2017) This article discusses a qualitative study done over wake therapy paired with the specific chronotherapeutic intervention, light therapy. This study is interesting to me as an interior design major, because behavioral healthcare design is becoming more and more popular. This is most likely an effect of the increased research that has been provided over these subjects.

Since depression is seemingly becoming a bigger and bigger issue, it must be taken into account more. As an interior designer, I must always keep the user in mind. Those who will be using the space that I create for them will be affected by whatever environment I design. I must put myself in the shoes of those who I will be designing for. As someone who has a mental health condition themselves, this is an issue that is easy to empathize with. Empathic design is something I am very interested in, so this information could be significant research information keep in mind for my future designs.

The authors of this research study created a clear and definitive need for this study. They clearly defined what depression is, and how many people it affects. The authors then provided examples of statistics on depression. This build-up lead into a detailed explanation of what wake therapy and light therapy interventions is. The author’s also described the research that is out today on such topics. They critiqued the studies that have already been done, and explained what they wanted to achieve. The authors were clear and to the point.

The author’s also made sure to control for a wide range of variables, which helped upstand the validity of this study. Depression has many uncontrollable variables, yet many methods were used to help prevent a bias with those uncontrollable variables. The authors were very picky with who they chose as participants. They did not allow those who had “severe suicidal ideation, panic anxiety and personality disorder, drug or alcohol abuse, psychotic disorder, pregnancy, glaucoma, epilepsy, and electroconvulsive therapy” to participate. (Kragh et al. 2017) In addition, the author’s made sure to be as inclusive as they could. They made sure that even though they had patients who had a depressive order as a part of a bipolar disorder, their “high” moods were adjusted for with mood-stabilizing therapy. (Kragh et al. 2017) The authors thoroughness helps justify their study.

While this study was very thorough, a key part could be portrayed as prejudiced. Since the participants knew the main interviewer previously, there is potential for those responses to have been swayed for many reasons. Allowing the study to be done by someone familiar was meant to help participants open up, however, it could have also made participants do the complete opposite. The first author was a nurse of the hospital ward chosen, so how participants felt about that first author previously, could have affected their responses. The first author could have also had a bias in what they documented in relation to their own preconceptions.

If I were to continue this study, I would do several things differently to assist in guaranteeing validity of the study. I would choose a site that is impartial to me. This way my results will not be seen as biased. To ensure that this study has a distinct focus I would consider more variables. I would not include patients with bipolar disorder. I would also restrict the age gap to be smaller, or to include several smaller studies with specific age groups. I would also consider using people who have had a shorter history of depression. A major theme that was seen was that the patients were nervous about this recovery option. If that fear could be eliminated or reduced, different results may have been reflected. Continuing this study would be interesting to me because I want to find out more specific information on how these concepts can be included in interior environments; questions related to the interior’s elements would be used to conduct my research.

Figures

Figure 1. Data collected and analyzed by this study. This table provides background information on participants and their emotional state. Kragh, M., Møller, D. N., Wihlborg, C. S., Martiny, K., Larsen, E. R., Videbech, P., & Lindhardt, T. Experiences of wake and light therapy in patients with depression: A qualitative study. International Journal of Mental Health Nursing, 2017. p. 172.

Figure 2. This picture shows the four sub-themes identified by this study under the main theme. Kragh, M., Møller, D. N., Wihlborg, C. S., Martiny, K., Larsen, E. R., Videbech, P., & Lindhardt, T. Experiences of wake and light therapy in patients with depression: A qualitative study. International Journal of Mental Health Nursing, 2017. p. 174.

Decision Support Systems 83 (2016) 47–56

Contents lists available at ScienceDirect

Decision Support Systems

journal homepage: www.elsevier.com/locate/dss

Online shopping intention in the context of data breach in online retail
stores: An examination of older and younger adults

Rajarshi Chakraborty a, Jaeung Lee a, Sharmistha Bagchi-Sen b, Shambhu Upadhyaya c, H. Raghav Rao d,⁎
a Department of Management Science and Systems, State University of New York at Buffalo, Buffalo, NY, USA
b Department of Geography, State University of New York at Buffalo, Buffalo, NY, USA
c Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA
d Department of Information Systems and Cybersecurity, University of Texas at San Antonio, San Antonio, TX, USA

⁎ Corresponding author. Tel.: +1 210 458 6300; fax: +
E-mail addresses: rc53@buffalo.edu (R. Chakraborty),

geosbs@buffalo.edu (S. Bagchi-Sen), shambhu@buffalo.ed
mgmtrao@gmail.com (H. Raghav Rao).

http://dx.doi.org/10.1016/j.dss.2015.12.007
0167-9236/

© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:
Received 24 November 2014
Received in revised form 8 November 2015
Accepted 25 December 2015
Available online 16 January 2016

Data breaches through hacking incidents have become a significant phenomenon in the world of online shop-
ping. These breaches can result in loss of personal data belonging to customers. This study builds a research
model to examine people’s intention to engage in e-commerce in the context of a significant data breach (the
Target breach in December 2013). In addition, this paper focuses on the difference in responses regarding
post-breach online shopping intent among younger adults (below 55 years) and older adults (senior
citizens—above 55 years). Our findings show the importance of internal (self) monitoring of bank transactions
in reducing the effect of perceptions of severity of data breaches on post-breach online shopping intent particu-
larly for senior citizens. The study also demonstrates that perceptions of severity of a hacking incident are signif-
icant drivers of perceived online shopping risk for both age groups. Further, perceptions of severity of a hacking
incident are significant drivers of post-breach online shopping intent but only marginally significant for younger
adults. Trusting beliefs in online shopping services and attitude toward e-commerce are significant for the older
generation for post-breach online shopping intentions and also for younger adults. Gender is significant for se-
niors while it is not significant for younger adults. The impact of perceived online shopping risk on post-breach
online shopping is significantly different between the two age groups. The implication of this research lies in
informing shopping websites the need to prepare better plans for notifying customers about not only data
breaches but also their proposed mitigation steps so as to increase trust and reduce perceived risks associated
with online shopping.

© 2016 Elsevier B.V. All rights reserved.

Keywords:
Online shopping
Data breach
Trust
Perceived risk
Internal monitoring
Age

1. Introduction

Online shopping has been steadily gaining acceptance around the
world, especially in the United States [19]. Online shopping websites
have in some instances replaced physical stores (e.g., books and
electronics) [53]. The rise in online shopping has partially been attribut-
ed to the success of secure payment methods through credit and debit
cards. According to recent findings [60], these cards account for over
70% of payment methods used for online shopping. In addition to the
increasing payment convenience, time-saving has also been a key factor
in the adoption of e-commerce [3]. Online stores have improved the
overall shopping experience by mimicking the amenities of a physical
shopping experience in a virtual world [69]. One such example would
be saving items in a “shopping cart” and checking out at a later point.
This workflow is akin to dropping objects in a physical shopping cart

1 210 458 6305.
jaeungle@buffalo.edu (J. Lee),
u (S. Upadhyaya),

and walking around the store until it is time to check out. This conve-
nience in online shopping experience has reduced the perceived risks
towards online shopping that could have been attributed to the lack of
physical tangibility [54]. While Amazon has been largely at the fore-
front, several traditional retail chains are now active online. Target,
Walmart, and Best Buy often experience as much traffic and transac-
tions through their websites as they do through their physical stores [5].

The literature on information systems and marketing is rich with
studies about the adoption and success of online shopping [12,39,52,
66]. Most of these studies have examined trust, convenience, and priva-
cy as antecedents. Trust and privacy concerns, in particular, have
remained of sustained interest given the ever-evolving risks and attacks
associated with online shopping over the years. According to statistics
released by the Identity Theft Resource Center [25], in the first half of
2014, 381 reported data breaches led to the exposure of over 10 million
records of individuals in the United States. This presents significant dan-
ger to personal and sensitive data stored in millions of websites. The
most danger is faced by websites that allow customers to make transac-
tions. These include shopping websites where hackers are still able to
get past sophisticated firewalls and other security software as was the

http://crossmark.crossref.org/dialog/?doi=10.1016/j.dss.2015.12.007&domain=pdf

mailto:mgmtrao@gmail.com

http://dx.doi.org/10.1016/j.dss.2015.12.007

www.elsevier.com/locate/dss

48 R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

case with Target in 2013 [68]. In addition to the technical challenges,
often personnel in charge of the security of these websites fail to take
pro-active actions. Customer names, addresses, email addresses,
account numbers, and transaction information are often exposed as a
result of many of these breaches [18].

Given that online shopping is an integral part of today’s economy, it
is important to examine how people’s attitude towards it is affected by
threats to their personal information. In this paper, we have specified
the study’s context as the Target data breach [68]. We evaluate the effect
of traditionally held notions of trust, security risk, and behavior in online
shopping [50]. This paper’s contribution is threefold. First, we have
incorporated people’s internal monitoring to scrutinize artifacts that
might be affected by data or other security breaches. We posit that mon-
itoring habits mitigate some of the security concerns and placate fears
that may arise in the aftermath of security incidents like the Target
and Neiman Marcus data breaches. Second, while purchasing intent
has been studied in the field of information systems, it has not been
explored in the aftermath of a major data breach incident that is likely
to alter intent. Third, the perspective of generational differences has
not been examined in prior literature. This paper looks at the difference
between two age-based demographics—one below 55 and the other
above 55 years. Traditionally, the latter is referred to as the “senior citi-
zens” generation (older generation) with those between 55 and
60 years representing the first tail of it. The reason for this comparative
approach is twofold: (1) research has shown significant differences in
privacy concerns between older and younger computer users [27] and
(2) testing our hypotheses on a younger sample gives us perspective
to interpret the findings from the older population. As our findings
will show later in this article, there are significant differences in certain
fundamental causality aspects of trust and risk-driven behavior on the
Internet between these two broad categories of the U.S. population.

To summarize, this paper focuses on the difference in responses re-
garding post-breach online shopping intent among younger adults
(below 55 years) and older adults (senior citizens—above 55 years).
Findings show the importance of internal (self) monitoring of bank
transactions in reducing the effect of perceptions of severity of data
breaches on post-breach online shopping intent, particularly for senior
citizens. The study also demonstrates that perceptions of severity of a
hacking incident are significant drivers of perceived online shopping
risk for both age groups, and while they are significant drivers of post-
breach online shopping intent for seniors, they are only marginally
significant for younger adults. Trusting beliefs in online shopping
services and attitude towards e-commerce are significant for the older
generation for post-breach online shopping intentions and also for
younger adults. Gender and the impact of perceived online shopping
risk on post-breach online shopping are significantly different between
the two age groups.

In the remainder of this paper, we first discuss recent data breaches
serving as the background of our study. Then we present the develop-
ment of our research model. After that, the data collection and the anal-
ysis are discussed, following which the paper concludes with a focus on
the current limitations and the opportunities for future research.

2. Prior literature

According to a report by the Identity Theft Resource Center (ITRC)
Dark_Reading [21], 73% (365 respondents out of 500 respondents) an-
swered that they may not purchase merchandise from online websites
that have experienced security breaches. Such incidents have triggered
customers’ protective behaviors such as avoiding using online stores,
switching to another online store, and using offline stores [41]. Khalifa
and Limayem [37] also found that customers will shop on e-commerce
sites more frequently if they do not worry about risks of security
breaches.

In addition to online shopping cases, offline business research has
also provided similar findings. Belanger et al. [7] studied the impact of

security breaches on hotel revisit intention, likelihood of hotel recom-
mendation and satisfaction. Their results showed that breaches resulted
in negative impacts on all outcome variables. This indicates that
consumers are highly concerned about data breaches.

Customers’ credit card information and other personal information
are some of the most commonly stolen items during data breaches into
the systems of shopping websites [57,73]. Upon a breach and an improp-
er access to such information, these customers become vulnerable to un-
approved purchases. Information like mailing address also has the
potential of being misused for exploits. Often, such exploits can be harder
to detect and their effects can be felt by victims in the real world. Online
transactions are seldom carried out with complete information about
privacy protection from the store owner [1]. On the other hand, cus-
tomers of online shopping are rarely given the option to choose what in-
formation they should provide to the website for the transaction and any
additional benefits. For example, storing information about one’s favorite
local store requires providing one’s zip code. Often, a transient piece of
information for the completion of a transaction may be enough to put a
customer at risk after a data breach. Thus, any data breach into these
businesses can potentially lead to identity theft.

Baier [4] defined trust as the “accepted vulnerability to another’s
possible but not expected ill will toward one” (p. 99). Customers
know the kind of risk they are taking; however, the individual customer
is often disposed to trust that nothing bad will happen to them. They
have positive expectations regarding online shopping websites in their
provisioning of shopping services. A fundamental antecedent of tech-
nology adoption [22] is the decision to trust a technological artifact.
Trusting or distrusting of an artifact is based on an individual’s general
disposition to trust others [47]. The Web Trust Model (McKnight et al.
[47] explains the causality of trust on behaviors in the form of decisions
made on the Internet. These decisions usually pertain to actions like
shopping and sharing information on the Internet. More recently, how-
ever, researchers have started to investigate these decisions in the light
of both negative and positive beliefs about the potential outcomes [45].
While most people may trust their frequently visited shopping websites
with respect to service quality, repeated stories of breaches may arouse
concerns and distrust among them. Media reports about breaches can
lead to a significant drop in consumers’ trust in the security-related ca-
pabilities of shopping websites. Whether trust and distrust are distinct
constructs or the opposites of a trust–distrust continuum has been de-
bated. Omodei and McLennan [56] proposed that trust and distrust are
two ends of the same scale. Luhmann [43] posited that while trust and
distrust are essentially the same construct, they are distinct functional
equivalents. Therefore, in our paper, we considered trust and distrust
(as a lack of trust) in the same construct.

Disposition to trust has been constantly changing through genera-
tions [38,51,67]. For instance, around 1996, Internet through the Web
(i.e. the Mosaic Web browser) became mainstream. People at that
time who were in their late 30s (i.e. 55 and above at the present time)
were the last generation to whom Internet was introduced as a niche
technology. Studies about differing perceived usefulness of IT across
age groups have shown that the general perception about the Internet
is different among people above 55 and those below [49]. According
to [64], younger Americans are less trusting of fellow human beings
than their older counterparts. [59] have shown that there is no signifi-
cant difference between younger baby-boomers and older baby-
boomers in terms of most behavioral variables. Also, traditionally, the
age of 55 years has been shown to be an important lower bound for
studying Internet behavior in the older population [70]. The fundamen-
tal concepts of trust and risk-taking are the differentiators between
younger adults and people over 55. In the context of online shopping,
given the importance of security and privacy, awareness about security
hazards can also be a significant differentiator. Grimes et al. [30] have
shown that older adults are generally less aware of security hazards
on the Internet compared to their younger counterparts. Also, older
adults tend to be generally more risk-avoiding than younger adults,

49R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

regardless of the domain [63]. In a similar vein to the above-mentioned
literature, we take a perspective of generational differences to explore
the relationship between risk and trust on online shopping decisions.

Of course, trust is not the sole predictor of online shopping behavior.
People may make a risky online purchase without trust or with a low
level of trust. For instance, other antecedents such as attitude towards
e-commerce may influence customers in their decisions regarding on-
line transactions. According to [65], attitude is a function of online
stores’ privacy policy where the organizations disclose their intentions
on how they will use their customers’ information. In the e-commerce
setting, the level of privacy protection perceived by customers can
counteract the negative effect of level of uncertainty in a transaction
[2]. For customers, it would be easier to accept and use the electronic
distribution channel when level of perceived attitude towards
e-commerce site is high.

The lack of transparency, especially in the immediate aftermath of a
security breach, often contributes to strains in the relationships
between shopping websites and their customers [29]. Customers may
discover identity theft and fraud through some irregularities or suspi-
cious transactions in their bank statements, especially if the banks do
not use sophisticated fraud detection systems. Other potential signals
of fraud may include getting locked out of one’s accounts and specula-
tive reports from the news media. If the bank of the affected payment
method fails to detect a fraud over a prolonged period after the breach,
then it is possible for a customer to feel repercussions through future
identity theft incidents. For every kind of potential fraud borne through
a data breach, proactive measures to stay alert can help.

Internal monitoring is an important element for online shoppers to
safeguard themselves against data breach, fraud, and identity theft.
One of the benefits of e-commerce and online banking is that customers
can check their accounts online at any time of the day from any location
in the world. This service is usually free of charge and can be an advan-
tage in coping with potential frauds. Checking one’s bank statements
regularly effectively gives customers a transparency to potential vulner-
abilities. According to [48], often individuals do not consider tracking
the credit report (a form of internal monitoring) as a preventative mea-
sure, in part, because of a lack of awareness. Since most purchases over
the Internet are done using credit cards, communications programs
from retail stores can be an important factor to alert consumers to be
watchful of, and monitor their credit records [58].

Thus, we consider monitoring and age demographics to be important
issues in the discussion of online decisions that are significantly influ-
enced by trust and risk perceptions. Monitoring is an active form of
awareness that, to our knowledge, has typically not been studied in ex-
aminations of phenomena about Internet and security, and is much
more important today since data breaches are commonplace now. On
the other hand, information asymmetry can reduce trust and increase
perceived risks about online shopping in the current security environ-
ment [50]. In this study, we investigate how security threats such as
data breaches, in spite of the opportunities to monitor, can potentially
impact a person’s intention to conduct business with a shopping website.

3. Research model

A person will have less concern about protecting information that
she/he does not deem valuable. Credit/debit cards and other personal
information are critical to customers’ well-being as their misuse can
cause material stress. When a shopping store’s database or any other in-
ternal system is breached, several information nuggets are at risk of
being exposed to hacker(s). These often include the username and pass-
word and several other personally identifiable information necessary
for shopping activities. Most people tend to reuse their passwords
across different online properties [35]. Hence, data breaches at a single
store can often expose several other online accounts of these customers.
Banks today are capable of alerting their customers about suspected se-
curity breaches and fraudulent uses of payment cards. In response to

such events, retail banks would usually either lock down the account
or cancel the payment card. Thus, for the customers, concerns pertain
more to the misuse of other information that hackers can get through
these data breaches.

Buffett et al. [9] have shown that not all individuals value the same
type of information equally. For instance, person A may not treat her
cell phone number as a sensitive information as much as person B
does. Person A is expected to feel less outraged than person B at an un-
authorized sharing of her cell phone number. In other words, not every
person should be expected to perceive the same level of severity of the
outcome of a data breach. Perceived severity in our study is a proxy for
the level of “seriousness” of a security issue at a shopping store, as per-
ceived by an individual. According to [55], a user’s perception of severity
about these outcomes should lead to behavior that prevents such out-
comes from materializing. In the context of our study, that implies stop-
ping or reducing e-commerce transactions with online stores that share
characteristics with Target. We thus hypothesize the following:

H1. Perceived severity of security breaches has a negative effect on post-
breach shopping intent.

Perceived severity of any kind of data breach in an e-commerce sys-
tem reduces the likelihood of shopping online. A similar impact is pos-
sible on engagement in activities that improve the shopping
experience. Such activities include saving payment information on the
website. We argue here that such fears can be countered by being pro-
active about security. This phenomenon is often seen in general Web
browsing. People are aware of hackers and their malicious activities
exploiting vulnerabilities. However, many of them tend to have that
fear reduced by proactively taking steps like installing anti-virus and
anti-spyware and often configuring firewalls. We argue there is a paral-
lel between this scenario and the one about shopping. The difference is
that anti-virus software blocks a security threat directly while being
alert about spending activities is part of a best-effort action to cope
with and monitor security threats indirectly. The recent spate of attacks
against stores like Target was not directed at specific customers. To cope
with the repercussions of these attacks, a specific customer can monitor
unusual spending since her payment method(s) may have been ex-
posed to malicious groups of people. This coping mechanism should
mitigate the “seriousness” aspect of the attack and affect her desire to
shop again. We thus hypothesize the following:

H2. Internal monitoring reduces the impact of perceived severity of securi-
ty breaches on post-breach shopping intent.

In the context of online shopping, these risks can arise in the context of
security, privacy, and quality of shopping service. The focus of our study is
the consequences of data breaches to customers of online shopping
stores. In that context, it is apparent that when a customer will sense a sig-
nificant risk of an online service being disrupted by malicious people, thus
possibly putting her personal information at a risk, she will tend to engage
less with such a website. This could be true in spite of her faith in the
shopping service-related attributes of the very same website. This con-
cern is magnified when several shopping websites, especially the high-
profile ones like Target, face disruptions and breaches. A magnified
sense of security risk faced by online stores is thus expected to reduce
the desire to pursue online shopping to an even greater degree.

In his classic article, Bauer [6] mentioned that perceived risk has
components of uncertainty—the likelihood of unfavorable outcomes,
and consequences—the importance of a loss [11]. This sense of risk is a
strong manifestation of the misgivings that a customer has in the secu-
rity of shopping websites in general, which we operationalize through a
construct called perceived online shopping risk (P

OSR

). POSR stems
from the uncertainty of data breach resulting in potential post-purchase
financial loss through potential violation of private information. Person-
al or financial information may be stolen from the websites’ database by
hackers. Consumers may not engage in online shopping, if personal

50 R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

information might be at risk and monetary loss can be a possible out-
come [42]. This reasoning leads us to the following hypothesis:

H3. Perceived online shopping risk has a negative effect on post-breach
shopping intent.

In the consumers’ mind, perceived severity of security breach mani-
fests itself in the fear of potential consequences of an event. Consumers
are afraid of the consequences that may create personal problems for
them. Consumers would have negative feelings towards these severities
when they know that one of the websites that they regularly visit to
shop online has suffered data breaches. For instance, [50] found that In-
ternet users’ top-three concerns with regard to online shopping were
privacy loss, system security breaches from third parties (due to faulty
technological security), and security breaches that included fraudulent
online retailer behavior. Personal information leaks, hackers’ intention
to harm the consumers, and banking information leaks due to the
website’s breached data storing system were all concerns of consumers.

Survey results cited by [7,33] support that consumers’ often referred
to reasons for rejecting online transactions were their concerns about
the lack of information privacy and the potential loss of control over
confidential information. Because e-services are based on the continual
transmission, processing, and storage of often sensitive financial or
personally identifiable information, many consumers may reject using
an e-service due to perceptions of risk [24]. A shopper’s belief about
shopping websites’ security vulnerabilities is a strong indicator of inher-
ent cynicism about online security in general. Thus, it is evident that
consumers’ perception about security beach in a website makes them
nervous about all the perceived risks associated with it. This brings us
to the following hypothesis:

H4. Perceived severity of security breach has a positive impact on per-
ceived online shopping risk.

E-commerce involves the primary activity of purchasing products
and services through a website by using an electronic payment method
like a debit/credit card. In order to engage in this activity, one has to get
comfortable with several components that collectively form one’s shop-
ping experience. The most important would be the fundamental idea
that one can buy something without walking into a store. Engaging in
transactions in the virtual world eliminates the opportunity to interact
face to face with store employees and to possibly test the product in per-
son. That can create a certain level of discomfort especially in generations
that did not grow up with such virtual shopping experiences. In addition
to the virtual aspect, payment and personal information are often han-
dled in unprecedented ways. In order to expedite the checkout process,
most shopping websites give the customers the opportunity to save per-
sonal information like name, address, email address, phone number, and
payment methods like credit or debit card information on the website.
For generations that grew up with cash transactions, this is sometimes
an unsettling experience, especially if the entity that is asking for such in-
formation does not have a human face to it. We operationalize this
comfort-level with some of the key aspects of e-commerce as E-
Commerce Attitude (EA). Attitude towards online shopping has previ-
ously been shown to be a strong indicator of actual online shopping be-
havior [52]. Knowledge about data breaches can reduce the keenness on
shopping online or affect the habit of saving important information on
websites. However, since attitude is one of the key indicators of behav-
ioral intention in online shopping [52], we suggest that a person demon-
strating a higher comfort level with most e-commerce attributes will be
less likely to be affected. We can thus hypothesize the following:

H5. E-commerce attitude has a positive influence on post-breach
shopping intent.

Online shopping-related risks include security risk and privacy risk
[23], which decrease the overall utility (benefit) the consumer obtains
from shopping on the Internet. The higher the consumer’s perception

of the risk associated with shopping on the Internet, the higher their
perception of the variance or uncertainty in the benefits derived
would be. If the consumers think shopping on the Internet is highly
risky, they would expect a large variance in the utility from shopping
on the Internet [8]. Once consumers have learned that online shopping
could produce negative consequences, they will avoid those conse-
quences by decreasing online shopping activity [16]. Consumers implic-
itly evaluate the relative worth or importance of benefits against the
cost (perceived shopping risk) of e-commerce to form a value assess-
ment. When such value assessment results in a perception of decreased
utility, it would have an impact on consumers’ attitude towards
e-commerce. Therefore, we propose the following relationship:

H6. Perceived online shopping risk has a negative effect on
e-commerce attitude.

Trust has been established as a strong predictor of attitude towards
many online behaviors, specifically online shopping [71]. With the
increased improvement in encryption technology, more shopping sites
are offering information storage options for customers both for the pur-
pose of improving their shopping experience and to increase their like-
lihood of returning. Those who feel most comfortable with these
information storage options are deemed to have a more positive atti-
tude towards e-commerce in general. Quality of shopping service, on
the other hand, may comprise of the browsing capabilities on the
website, the transaction process and finally the matching of the quality
of the delivered product or service to that what the customer expected.
When a shopping site scores high on all of these quality-related attri-
butes, it is natural for the customer to have a higher trusting belief in
that website and the store in general. This positive belief and expecta-
tion should naturally increase the comfort level of customers with a spe-
cific website as well as online shopping in general. Based on this
reasoning, we hypothesize the following:

H7. Trusting belief in shopping services has a positive effect on
e-commerce attitude.

A consumer’s trust in shopping websites can stem from her experi-
ence with the overall service being offered by the store even though
the website is simply a gateway to it. This trust is often a strong indicator
for the return of customers to the website for future purchases. Most of
the successful shopping stores that operate both through a website as
well as through a physical store have good reputation for conducting
their business ethically given the enforceable customer-protecting
legal frameworks in play today. With this reality, it is seldom a trend
where multiple customers over a prolonged period feel “ripped off” or
come out dissatisfied with the service of a shopping store. As for the on-
line version of this experience, customers feel their trust in the service
quality is often reduced by the experience of browsing the website
and/or from the dissatisfaction with the product bought or the handling
of the payment. Often these service quality-related factors compete
strongly with security and privacy concerns about the retail outlet for
that service, especially since for many people, security or privacy as-
pects do not completely define the shopping experience. In fact, a
website could potentially reduce the quality of the shopping experience
by implementing a security or privacy feature that may hinder the
workflow of the customers. When a website, that one does not neces-
sarily shop at, gets impacted by a security incident, a consumer is less
likely to be affected by security principles. Given that literature [72]
has shown that trusting beliefs are a major indicator of adoption of tech-
nology, we hypothesize the following:

H8. Trusting belief in shopping services has a positive effect on post-breach
shopping intent.

The constructs involved in the proposed hypotheses are operational-
ized by the variables defined in Table 1. The following section describes

Table 1
Variables for the research model.

Variable name Definition of variable Measurement

1 Post-breach online shopping (PBOS)
Likelihood of continuing shopping and showing favorability towards a shopping website like
Target.com in the light of the data breach at Target.com

[34]

2 Perceived severity (PS) What would be at stake for a person if one of her shopping websites faced a hacking incident [55]
3 E-commerce attitude (EA) General comfort level with practices and habits commonly associated today with online shopping Self-developed
4 Perceived online shopping risk (POSR) Perceived risks that shopping websites and online shoppers in general face today [17]
5 Trusting beliefs in shopping services (TBSS) Trust that a user may have in the quality of shopping service offered by shopping websites in general [40]
6 Internal monitoring (MON) Person’s frequency of checking her bank statement(s) Self-developed

51R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

how these variables were measured. The variable relationships taken
together explain the underlying social and environmental conditions
that “cause” a certain social behavior to happen. The research model is
depicted in Fig. 1.

4. Data collection

The data were collected one month after the massive Target data
breach attack that happened in mid-December 2013.1 We created a
survey and got IRB permission to launch it. We collected data through
an online survey conducted by Qualtrics. Qualtrics found eligible re-
spondents (young adult b55 and senior citizen ≥55) based on a random
sampling of their national database and distributed the survey to each
group. Specifically, Qualtrics contacted individuals through email an-
nouncements in their database that matched our criteria. Each individ-
ual respondent could choose the time and place to respond. Moreover,
respondents could withdraw from the survey any time without any
penalty. As we were interested in differences between below 55-year-
old and above 55-year-old (senior citizens) groups of the U.S. popula-
tion, we conducted two surveys. The sample size from the former was
159 while that from the senior citizens’ category was 205. Henceforth,
in the remainder of this paper, we shall refer to the two datasets as
the “younger” and the “senior (or older) citizens” datasets, respectively,
for convenience. For both the samples, we used a filter that screened
respondents—they must have shopped online at least once in their
life. Not satisfying this filter would have rendered the rest of the survey
instrument meaningless and not applicable to this study.

The online survey was utilized to empirically test the research hy-
potheses presented above. The constructs were measured based on
self-reported scores through the online questionnaire. Most of the
items used to measure the constructs in the model were borrowed/or
adapted from literature and all variables were measured on a 5-point
Likert scale. Items for perceived online shopping risk (POSR) [17] as
well as those for trusting beliefs in shopping services (TBSS) [40] were
adapted to the context of online shopping. Perceived severity (PS) was
measured by adapting items for online shopping from [55]. We mea-
sured internal monitoring (MON) through two self-developed 5-point
Likert scale questions. In order to develop internal monitoring measure-
ments, we conducted interviews with two information security profes-
sionals in a local bank. Based on the interviews, we created two
measurements. (1) I sign up for spending alerts for all my payment
cards and (2) I track the charges on my bank statement. However,
only one of them turned out to load significantly both in factor analysis
and PLS (described later). The significantly loading item for MON mea-
sured the frequency of checking one’s bank statements, which was used
as a proxy for wanting to stay aware of misuse of credit or debit cards.
The dependent variable, post-breach online shopping (PBOS) intent,
measured intent of continuing online shopping activities as usual, by
using items directly from [34].

In each of the online surveys, the items for the dependent variable,
PBOS, were presented on a screen immediately after the subjects were
shown a sample email sent from a prominent bank (name disguised)

1 https://corporate.target.com/about/shopping-experience/payment-card-issue-faq

to its customers right after the Target data breach of 2013. The e-mail
alerted customers of the bank about the Target breach and that the
bank was monitoring customer accounts for suspicious activity. Respon-
dents were asked to answer the PBOS items keeping this incident and a
retail chain/store in mind that operates like Target (i.e. allows shopping
both through a website as well as through physical stores). Right after
the survey items, the respondents were asked to verify which website
they had in mind when responding to the PBOS items. We called this
item TAR. As will be shown in the results of our PLS analysis later, con-
trolling for TAR showed that a significant difference in the scores for our
dependent variable for both the datasets.

A majority of the senior citizens respondents were in the 65–
69 years age group (27%), while in terms of gender the dataset was
evenly split (51% females). Among the younger group, a majority of
the responses came from the range 18–30 year olds (40%), with gender
equally split there as well. All respondents had engaged in online shop-
ping at least once in their life. While a few of the younger subjects (13%)
had not heard of the Target data breach (others had, thanks to the media
coverage), only 5 out of 205 senior citizens respondents were not aware
of this incident. That was reflected in the fact that 32.7% of the younger
subjects and 47.8% of the senior citizens subjects confirmed that they
had assumed Target as an example store when responding to items
measuring PBOS.

We found that 29% of the younger and 24% of the senior citizens sub-
jects had shopped at a website that at least once faced a hacking inci-
dent. Among the younger subjects, 13% had fallen victim to Internet
scams in general and 14% were victims of some form of identity theft.
The numbers on the senior citizens side were a little on the higher
side (i.e. 19% and 17%, respectively). The difference between these two
broad age groups in terms of computer courses and workshops taken
was slightly different. A total of 45% of the younger population had par-
ticipated in such courses while for the senior citizens, that number was
39%.

5. Analysis and results

The research model (Fig. 1) proposed earlier was evaluated using
partial least squares (PLS) [13]. This method has been shown to be
robust to reasonable sample sizes [10] and lack of normality for most
variables in a dataset [15]. PLS regression was conducted on the two
groups of survey-generated datasets (young and senior citizens) using
SmartPLS [62], a tool that has been widely adopted by both information
systems [44,61] and marketing [32] researchers. For each variable in the
path model, we chose a reflective measurement model [20,28]. Table 4
in Appendix A presents survey items what we used for our path model.
In addition, as seen in the tables (Tables 5 and 6) in Appendix B, both
analyses resulted in satisfactory quality criteria (i.e., AVE, communality,
composite reliability, and Chronbach’s alpha) [31]. In addition, the out-
put in both cases satisfied the Fornell–Larcker condition [26] demon-
strating the discriminant validity of our survey instrument. While the
overall quality of both the models was satisfactory, the path coefficients
and the R2 values of the endogenous variables were found to be quite
different. The path coefficients of the PLS analysis for both the models
along with their significances are given in Table 2 below. The same

http://Target.com

http://Target.com

https://corporate.target.com/about/shoppingxperience/paymentardssueaq

Fig. 1. Research model.

52 R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

has been depicted on the path analysis diagrams in Appendix B. (Figs. 2
and 3).

In the third to the last row of Table 2, we have included the path co-
efficient from a variable called TAR to our dependent variable PBOS. In
the PLS analysis with both datasets, we found a statistically significant
negative influence of TAR on PBOS. TAR is an indicator variable
confirming if the respondent kept Target in mind when responding to
the questions measuring PBOS. This was a way of verifying that the sam-
ple email from the bank was a reminder of the Target data breach that
was strong enough to influence the respondent to think about her cur-
rent shopping activities with respect to Target. We found, as the table
and the diagrams in Appendix B show, that for both age groups, there
would be a significant reconsideration taking place in the minds of the
respondents had they placed themselves in the shoes of a Target cus-
tomer. This finding perhaps shows the effect of empathy on shopping
intent. We used the following controls: education level (measured on
a 1–4 level, with 1 being high school), gender, and assumed target.

We analyzed the model with respect to each age group (i.e. below 55
against above 55) in our research model. This effect was examined using
multigroup analysis in PLS [14,36]. This method performs a t-test
between the pair of corresponding path coefficients for the groups. For
example, the coefficients of the path representing hypothesis 1
(PS → PBOS, Table 2) for the older and the younger adults were used
in a t-test. The t-value and its corresponding p-value indicate the statis-
tical significance of the difference between the two path coefficients.
The formula [14] for computing this t-value is as follows:

t ¼ Pathsample1−Pathsample2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
m−1ð Þ2

m þ n−2 •s:e:
2
sample1 þ

n−1ð Þ2
m þ n−2 •s:e:

2
sample2•

ffiffiffiffiffiffiffiffiffiffiffiffiffi
1
m

þ 1
n

rs ∼tmþn−2

where m is the sample size of sample 1 (i.e. older adults) and n is the
sample size of sample 2 (i.e. younger adults). The t-value thus computed
has a degree of freedom.

m + n − 2. “s.e.” refers to the standard error for the particular path
under consideration in each sample. These values are obtained from the
re-sampling procedure called bootstrapping implemented in SmartPLS
which gives the standard error for each path. Based on the datasets ob-
tained through our online survey, Table 3 shows the outcome of the
pairwise multigroup analysis of our research model between senior
citizens and younger adults.

The results in Table 3 show that the two populations are distinct in
some ways. Significant differences between the two populations pertain
to perceived online shopping risk (POSR). This implies that someone
who is fearful of risks associated with online shopping is more affected
by actual incidents. Thus, it appears that younger adults who have gen-
erally been less worried about risks may have decided to engage in

online shopping for other reasons than older adults. Perceived severity
and risk are thus more important drivers of online shopping behavior
after incidents for the older generations. The path coefficient is larger
(in absolute value) for senior citizens for H3 (POSR → PBOS). In addition,
the effect of gender was significantly different on post-breach online
shopping behavior between two groups. For younger adult groups, gen-
der did not show any statistically significant difference on PBOS. How-
ever, for the senior citizen group, gender showed significant results.
Especially, on average (for older adults), the female group had 0.164
higher PBOS than the male group.

6. Discussion

In this section, we discuss the findings, the rejection of certain hy-
potheses in both age groups, and the dissimilarity in significance of
path coefficients between the two. To summarize, the results tell us
that the research model we proposed explains an older adult user’s re-
sponse to Target and other breaches much better than it does for people
below the age of 55.

One of the key areas where younger people differed from their older
counterparts is the rejection of hypothesis H2. This hypothesis implies
that monitoring habit negatively influences effect of perceived severity
on shopping intent after being reminded about the Target incident. As
per our discussions earlier, monitoring, measured by regularity of keep-
ing an eye on bank statements, is an indicator of security consciousness
that is supposed to mitigate the feeling of threat and uncertainty. The
latter arises not only when a shopping website cannot be transparent
about the shopping experience, but also when news of data breaches
in retail chains is floating around. It appears from the PLS output that se-
curity consciousness or simply monitoring (MON) cannot temper this
fear in younger people. We can speculate here that this is an indicator,
in turn, of their awareness of the relative lack of usefulness of monitor-
ing in trying to actually curb threats. In other words, monitoring can
give one person a mental satisfaction but people familiar with the work-
ings of shopping and the Web in general may realize that breaches are
often inevitable or not easily stoppable.

Prior literature [17,46] has demonstrated the negative impact of
perceived risks to security, privacy, and service on intentions related
to online shopping. This negative impact is significant among the
older generations as opposed to their younger counterparts. To under-
stand this difference, we need to draw attention to the actual construct
being evaluated here as a dependent variable. PBOS intent is measured
in the context of a massive data breach and in the light of its potential
impact on Target’s customers whereby a bank had to send out mass
email alerts. This knowledge can very well orient the thought process
of a respondent towards a more risk-averse position regarding online
shopping. We did not see significant negative effect of the perceived

Table 2
Results from SmartPLS.

Senior citizens (≥55) Younger (b55)

Hypotheses R2 R2 Hypotheses

H1(−): PS → PBOS −0.168* PBOS 0.390 PBOS 0.181 −0.185^ (−): PS → PBOS
H2(−): MON → H1(−) −0.215* −0.036 (−): MON → H1(−)
H3(−): POSR → PBOS −0.100^ 0.064 H3(−): POSR → PBOS
H4(+): PS → POSR 0.201* EA 0.243 EA 0.178 0.405*** H4(+): PS → POSR
H5(+): EA → PBOS 0.333*** 0.246* H5(+): EA → PBOS
H6(−): POSR → EA −0.146* −0.159* H6(−): POSR → EA
H7(+): TBSS → EA 0.441*** POSR 0.040 POSR 0.164 0.372*** H7(+): TBSS → EA
H8(+): TBSS → PBOS 0.151* 0.137^ H8(+): TBSS → PBOS
TAR → PBOS −0.181* −0.144* TAR → PBOS
EDU → PBOS −0.013 −0.042 EDU → PBOS
GEN → PBOS 0.164* −0.007 GEN → PBOS

^p b 0.10, * p b 0.05, ** p b 0.01, *** p b 0.001.
PS, perceived severity; MON, internal monitoring; POSR, perceived online shopping risk; PBOS, post-breach online shopping; EA, e-commerce attitude; TBSS, trusting beliefs in shopping
services; TAR, assumed target; EDU, education; GEN, gender.

53R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

risk on the dependent variable for young adults. However, this was sig-
nificant for the older generations.

The final aspect where the output from the two samples differed was
the impact of trusting beliefs of shopping services (TBSS) on PBOS. In
traditional trust literature, this relationship is well known. However,
our data shows that when the end result is a behavior in the aftermath
of a security threat, this relationship may not hold especially with a
modern Internet-savvy population A high level of trust in the shopping
service offered by a website does not automatically translate to
intended purchase and other shopping-related activities especially
with sites that resemble Target.com, now that so many data breaches
have come to the public’s knowledge. The finding from the analysis
with the senior citizens dataset indicates the opposite—i.e. senior citi-
zens treat shopping service and security aspects separately. This differ-
ence between the two age categories deserves further investigation.

The practical implication of this study lies in its application in policy-
making. One of the findings from our survey is that perceived online
shopping risk is a stronger determinant of post-breach shopping inten-
tions in older adults. This implies that in the event of any large-scale
data breach at a popular organization, one of the tasks to undertake is
for breached organizations to convince their older clientele about the
security measures that can prevent any such future breach. Such steps
would help ensure continued business and transactions from this
demography. The importance of internal monitoring also implies that
e-commerce organizations can benefit by promoting campaigns about
the value of staying alert about online transactions. The increased prac-
tice of doing so in the form of checking bank statements would again
help ensure continued engagement even after a breach has occurred.
This would presumably be more than likely to be true with senior citi-
zens. In addition, for the relationship between PS and PBOS, we found

Table 3
Multigroup analysis between senior citizens and younger adults.

Path t-statistic 2-sided p-value Significance

H1 PS → PBOS 0.310 0.757 NS
H2 MON → (PS → PBOS) 0.841 0.401 NS
H3 POSR → PBOS 2.103 0.036 5%
H4 PS → POSR 1.505 0.133 NS
H5 EA → PBOS 0.617 0.538 NS
H6 POSR → EA 0.161 0.872 NS
H7 TBSS → EA 0.446 0.656 NS
H8 TBSS → PBOS 0.015 0.988 NS

TAR → PBOS 0.340 0.734 NS
EDU → PBOS 0.230 0.818 NS
GED → PBOS 2.275 0.024 5%

NS, not significant; PS, perceived severity; MON, internal monitoring; POSR, perceived on-
line shopping risk; PBOS, post-breach online shopping; EA, e-commerce attitude; TBSS,
trusting beliefs in shopping services; TAR, assumed target; EDU, education; GEN, gender.

significant effect for older adults and marginally significant effects for
young adults groups. That means the customers’ perception of severity
(PS) is significant when it comes to their likelihood to continue online
shopping even after a data breach. The result of younger adult groups
is interesting. We hypothesized that PS had a negative impact on
PBOS. Readers would be somewhat surprised to see this finding that
perceived severity of younger adults group is only marginally significant
on PBOS. Our recommendations and implications could apply more di-
rectly to online shopping and retail organizations that happen to have
a brick-and-mortar presence.

7. Conclusion

This study has a few limitations, one being that we were not able to
conduct a longitudinal study whereby we could have tested the online
shopping intent (PBOS) both before and after a certain data breach.
While it is not possible to anticipate a particular data breach incident,
these incidents keep happening often. A longitudinal study is thus in-
deed possible where a post-event survey would be conducted as soon
as a breach has been announced on some of the media outlets. A pre–
post study would also be possible to be conducted entirely after the
breach if we can select appropriate samples to control for the awareness
of a certain breach. This was not possible with something as mainstream
as the Target breach.

Further, we have divided the entire population into two broad age-
based categories. This type of grouping glosses over the uniqueness
that may be associated within certain age-based cohorts. While we
have theorized our model from an age perspective (senior citizens
against others), that is still too broad given that not every cohort within
either category were introduced to computers and the Internet at the
same time. However, this richness can be elicited only from a much larg-
er sample that can guarantee sufficient power within each age cohort
and this was something that could not have been achieved at this
time due to resource constraints. One more aspect about sampling
that limits our study is that we did not ensure to pick both Target and
non-Target customers in equal measures in order to control for the per-
sonal stake in the fallout from the data breach at that chain. This objec-
tive is difficult to fulfill as we cannot anticipate which store’s website
will be breached.

Other limitations include not considering the income or socio-
economic status of our subjects for the model. Before making a decision
about online shopping, a customer may do a cost–benefit analysis. This
analysis may differ from person to person depending on her economic
status and on the price and value of the product(s). We plan to test
this aspect in a future study. Internal monitoring in our study is essen-
tially the frequency of keeping track of charges. This requires a cognitive
burden and in the survey, we did not ask questions that would elicit
such burdens. The lack of constructs and items measuring the extent

http://Target.com

Fig. 2. Path analysis result for young adults.

54 R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

and nature of online shopping is a limitation in our study. Including such
items, however, would have increased the length of our survey ques-
tionnaire. Long questionnaires have been shown to evoke less thought-
ful responses.

To summarize, in this paper, we have presented a very topical inves-
tigation of attitude and intentions about online shopping activities under
the awareness of recent large-scale data breaches at major retail stores
like Target and Neiman Marcus. While privacy and security concerns
have been historically studied as some of the key antecedents to the
adoption of online shopping, in a 21st century environment, that adop-
tion has almost crossed the tipping point. It is thus now important to un-
derstand how the adoption will change given the spate of attacks that
online properties around the world are facing from organized and indi-
vidual cybercriminals. Our investigation has practical implications due
to its focus on the generational difference in perception of these modern
security risks and how they are channeled towards altering shopping-
related decisions. By incorporating personal actions like bank account
monitoring, though limited in its usefulness, we have put forward a richer
understanding of the security and privacy focus in the online shopping
context. Our research model was built on top of extant literature and
pieced together from fundamental theories about trust and risk. This
model has been validated using data collected from an online survey
and the striking differences observed between the senior citizens gener-
ation and the rest of the U.S. population should serve as a foundation for
further age-based technology adoption research.

Acknowledgement

The authors would like to thank the SE and review team for critical
comments that have greatly improved the paper. This research was
funded by the National Science Foundation (NSF) under grants
0916612 and 1227353. We would like to thank Md Shamim Akbar for
research assistance. The usual disclaimer applies.

P
M
E
P
P

Fig. 3. Path analysis result for senior citizens.

Table 5
PLS model overview for data from young adults (b55).

Ave.

Composite reliability Cronbach’s alpha Communality

POSR 0.628191 0.871060 0.804686 0.628191
MON 1.000000 1.000000 1.000000 1.000000
EA 0.660752 0.853814 0.753090 0.660751
PBOS 0.741735 0.934877 0.913401 0.741735
PS 0.717978 0.883998 0.802111 0.717978
P

S * MON

0.775660 0.911751 0.890022 0.775662
TAR 1.000000 1.000000 1.000000 1.000000

TB

SS 0.617098 0.827235 0.704771 0.617098

Appendix A

Table 4
Survey instrument.

Post-breach online shopping (PBOS) [34]:
PBOS1: I am likely to make another purchase from that website in the next year.
PBOS2: I intend to continue using that website rather than discontinue its use.
PBOS3: I will recommend that website to my friends.
PBOS4: I will recommend that website to my family.
PBOS5: I would reconsider saving any payment-related information on any
shopping website in general.

Internal monitoring (IM):
IM1. I track the charges on my bank statement.

Perceived severity (PS) [55]:
If a website where I do online shopping faced a hacking incident, it would…
PS1: …be a serious problem for me.
PS2: …have a negative effect on my shopping activities.
PS3: …have a negative effect on my payment card (credit/debit) use.

Perceived online shopping risk (POSR) [17]:
POSR1: Online shopping websites are vulnerable to hackers who may steal
customers’ information.
POSR2: Online shopping customers often face damaging and harmful behavior
from hackers.
POSR3: Customers’ information stored at online shopping websites is not safe.
POSR4: Customers are vulnerable in online shopping websites that had incidents
of hacking.

Trusting beliefs in shopping services (TBSS) [40]:
TBSS1: I believe online shopping websites provide services as expected.
TBSS2: I have positive expectations regarding online shopping websites in their
provisioning of shopping services
TBSS3: I am able to use online shopping services with confidence.

E-commerce attitude (EA):
I am comfortable ____________…
EA1: …shopping on the Internet.
EA2: …saving credit/debit card information on a shopping website.

EA3: …saving any personal information on a shopping website.
Website assumed (TAR):
TAR1: Which shopping website did you have in mind when you answered the
questions on the previous screen?

Appendix B. PLS results

PLS path model for data from young adults (b55)
(Sample size: 159)

Table 4 (continued)

PLS path model for data from senior citizens (≥55).
(Sample size: 205).

Table 6
PLS model overview for data from senior citizens (≥55).

Ave.
Composite reliability

Cronbach’s alpha

Communality

OSR

0.663836

0.944608

0.837023

0.663836

ON

1.000000

1.000000
1.000000
1.000000

A

0.656210

0.851100

0.747802

0.656209

BOS

0.741735

0.944608

0.926726

0.773296

S

0.773296

0.898658

0.830632

0.747314

T

55R. Chakraborty et al. / Decision Support Systems 83 (2016) 47–56

able 6 (continued)

P
TA

Ave.
Composite reliability
Cronbach’s alpha
Communality
S * MON

0.714017

0.881973

0.800875

0.714017

R

1.000000
1.000000
1.000000
1.000000

SS

0.740742

0.895368

0.826951

0.740742
TB

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Rajarshi Chakraborty Rajarshi Chakraborty received his PhD in Management Science
and Systems at the University at Buffalo (UB). His doctoral dissertation topic was on
online privacy for older adults. His other research interests include cyber security, in-
formation processing in disaster management, and cloud computing. He is a member
of the International Federation for Information Processing (IFIP) Working Group
8.11/11.13 (Information Systems Security Research). Rajarshi has published in the
proceedings of the Americas Conference on Information Systems, AIS SIGSEC’s Work-
shop on Information Security and Privacy IEEE IT Professional and Decision Support
Systems.

Jaeung Lee Jaeung Lee is a PhD candidate in the Department of Management Science and
Systems at the State University of New York at Buffalo. His primary areas of research inter-
ests include information security, emergency response management systems, and re-
quirements management. His research has appeared in Information Systems Frontiers
(ISF) and conference proceedings such as AMCIS 2015, Web 2015, SKM 2014, WMSC
2011, and IRM 2011.

Sharmistha Bagchi-Sen Sharmistha Bagchi-Sen is a Professor and the Chair of the Depart-
ment of Geography at the University at Buffalo (SUNY). Her research interests are Urban
and Regional Analysis International Business: Foreign Direct Investment, High Technology

and Regional Innovation Biotechnology and Pharmaceutical Sectors, Labor and Societal
Impacts of Information Technology, and Labor Market and the Aging Workforce. Her stud-
ies focus primarily on the United States and South Asian countries. She is also currently a
visiting professor at the University of Gothenburg, Sweden. In the past, she has been a co-
director of the Institute for Research and Education on Women and Gender at the Univer-
sity at Buffalo. Sharmistha has published in several outlets of her research interests includ-
ing European Planning, Applied Geography, and Computers in Human Behavior.

Shambhu Upadhyaya Shambhu J. Upadhyaya is Professor of Computer Science and Engi-
neering at the State University of New York at Buffalo where he also directs the Center of
Excellence in Information Systems Assurance Research and Education (CEISARE), desig-
nated by the National Security Agency. Prior to July 1998, he was a faculty member at
the Electrical and Computer Engineering Department. His research interests are informa-
tion assurance, computer security, fault diagnosis, fault tolerant computing, and VLSI test-
ing. He has authored or coauthored about 250 articles in refereed journals and conferences
in these areas. His current projects involve insider threat modeling, intrusion detection, se-
curity in wireless networks, and protection against Internet attacks. His research has been
supported by the National Science Foundation, Rome Laboratory, the U.S. Air Force Office
of Scientific Research, DARPA, National Security Agency, IBM, Intel Corporation, and Harris
Corporation. He is a senior member of IEEE.

H. Raghav Rao Professor Rao is AT&T Chair Professor at University of Texas at San Antonio,
on leave from UB as a SUNY Distinguished Service Professor of MSS at UB, USA and was
WCU Visiting Professor of GSM at Sogang University, S. Korea. His interests are in the areas
of management information systems, decision support systems, e-business, emergency
response management systems, and information assurance. He has also received the Ful-
bright Fellowship in 2004. He is an advisory editor of Decision Support Systems, co-editor-
in-chief of Information Systems Frontiers, AE of ACM Transactions in MIS, and senior editor at
MISQ. Dr. Rao also has a courtesy appointment with Computer Science and Engineering as
adjunct Professor.

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• Online shopping intention in the context of data breach in online retail stores: An examination of older and younger adults

1. Introduction
2. Prior literature
3. Research model
4. Data collection
5. Analysis and results
6. Discussion
7. Conclusion
Acknowledgement
Appendix A
Appendix B. PLS results
References