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R E S E A R C H Open Access
Fraud detections for online businesses: a
perspective from blockchain technology
Yuanfeng Cai
1
and Dan Zhu
2*
* Correspondence:
dzhu@iastate.edu
2
College of Business, Iowa State
University, Ames, IA, USA
Full list of author information is
available at the end of the article
Abstract
Background: The reputation system has been designed as an effective mechanism
to reduce risks associated with online shopping for customers. However, it is
vulnerable to rating fraud. Some raters may inject unfairly high or low ratings to the
system so as to promote their own products or demote their competitors.
Method: This study explores the rating fraud by differentiating the subjective fraud
from objective fraud. Then it discusses the effectiveness of blockchain technology in
objective fraud and its limitation in subjective fraud, especially the rating fraud.
Lastly, it systematically analyzes the robustness of blockchain-based reputation
systems in each type of rating fraud.
Results: The detection of fraudulent raters is not easy since they can behave
strategically to camouflage themselves. We explore the potential strengths and
limitations of blockchain-based reputation systems under two attack goals:
ballot-stuffing and bad-mouthing, and various attack models including constant
attack, camouflage attack, whitewashing attack and sybil attack. Blockchain-based
reputation systems are more robust against bad-mouthing than ballot-stuffing fraud.
Conclusions: Blockchain technology provides new opportunities for redesigning the
reputation system. Blockchain systems are very effective in preventing objective
information fraud, such as loan application fraud, where fraudulent information is
fact-based. However, their effectiveness is limited in subjective information fraud,
such as rating fraud, where the ground-truth is not easily validated. Blockchain
systems are effective in preventing bad mouthing and whitewashing attack, but they
are limited in detecting ballot-stuffing under sybil attack, constant attacks and
camouflage attack.
Keywords: Blockchain, Fraud detection, Rating fraud, Reputation systems
Background
Nowadays the Internet permeates our daily lives. With the fast-growing information
technology, the cyber world has transformed itself into a dominant platform, where
people can exchange information, conduct business, and connect with others from all
over the world. For example, Amazon had more than 285 million active users by 2015
(Lindner 2015). With the availability of unprecedented amounts of information, the
Internet provides convenience to its users. Additionally, it produces challenges for
users in processing information. Consequently, intelligent systems are widely applied
to assist users in decision-making.
Financia
l
Innovation
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
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indicate if changes were made.
Cai and Zhu Financial Innovation (2016) 2:20
DOI 10.1186/s40854-016-0039-4
With built-in artificial intelligence in different knowledge domains, intelligent systems
are capable of gathering information, processing problems, drawing inferences, and
generating solutions (Krishnakumar 2003). Given input information and different built-
in algorithms, intelligent systems can be applied to support decision making in various
domains, such as finance, e-commerce, and healthcare. Regardless of the problem
domain, the decision made by the intelligent system depends on users’inputs. There-
fore, decision accuracy is vulnerable to fraudulent users’input, which is termed as
information fraud. Unlike the real world, information in the cyber world is often input
through a Web interface. With the advances in Web technology, users can inject
fraudulent information easily, in various locations and without face-to-face interaction,
making it both difficult and costly to detect fraud. As such, information fraud can hurt
the effectiveness of intelligent systems, impair interaction trust in the cyber world, and
result in financial losses.
Scholars have long examined information fraud. Since the built-in algorithms in intel-
ligent systems are different, users also behave differently to inject fraudulent informa-
tion. Consequently, various types of information fraud have been identified and
summarized. Different supervised or unsupervised learning algorithms of fraud
detection have been designed for each fraudulent scenario (Irissappane et al. 2012;
Jøsang and Ismail 2002; Lee and Zhu 2012). Prior research has significantly improved
the accuracy of information fraud detection; however, few models can maintain perfect
detection performance in all fraudulent scenarios. Additionally, even if the fraudulent
input is successfully identified, we may still not be able to access the truth and make
the right decision. Furthermore, there are various reasons behind information fraud,
such as concerns for personal privacy or seeking inappropriate profits (Lam and Ried
2004; Metzger 2004). The current detection algorithms cannot completely eliminate
such behaviors.
In the era of decentralized computing, a breakthrough in blockchain technology,
which underlines Bitcoin (Nakamoto 2008), can be used to preserve users’privacy and
prevent information fraud. Blockchain is a public ledger that verifies every transaction,
stores it based on group consensus, and records it indisputably (Soska and Christin
2015; Vandervort 2014). As it can provide transaction records permanently, incorrupt-
ibly, and irreversibly, it may help fundamentally prevent some types of information
fraud (Khan 2015, Pwc 2015). In this paper, we analyze the effectiveness of blockchain
technology in fraud detection. While there are various types of information fraud, in
this study, we focus on one popular type: rating fraud. We consider a piece of informa-
tion fraudulent as long as it is not consistent with real information.
In the subsequent section, we provide a brief introduction of rating fraud, followed
by an overview of the literature on blockchain technology in An overview of blockchain
technology section. In Effectiveness of Blockchain on Rating Fraud section, we discuss
the effectiveness of blockchain technology on rating fraud. We conclude the paper in
Conclusions and Discussion section with discussions.
An introduction to rating fraud
Online interactions with anonymous users can involve risks. In the real world, we can
obtain feedback about a seller from previous customers before making a purchase. As
such, people tend to purchase from highly reputed sellers, since purchasing products
Cai and Zhu Financial Innovation (2016) 2:20 Page 2 of 10
from an unreliable seller may result in severe losses. Similarly, in the cyber world, we
prefer to pre-evaluate the trustworthiness of a potential seller with support from repu-
tation systems, as they are designed to help people to judge the quality of unknown
vendors beforehand.
Reputation systems collect, aggregate, and distribute feedback about entities’past
behaviors (Resnick et al. 2000). Theoretically, reputation is a distribution of opinions,
estimations, or evaluations about an entity in an interest group (Bromley 2001). An
interest group is one where the people within a group have some relationship or
concern with an entity (Bromley 2001). Reputation systems have been validated as
highly effective ways to protect customers from transactional risks. Numerous studies
have shown that reputation systems are effective to reduce transitional losses, improve
customers’buying confidence, help them make purchase decisions, and drive sales
growth for sellers (Ba and Pavlou 2002; Bolton et al. 2004; Park et al. 2007). Despite
their effectiveness, they are vulnerable to rating fraud, a phenomenon wherein raters
benefit themselves by creating biased ratings (Cai and Zhu 2015; Mayzlin et al. 2014).
Rating fraud is common in the cyber world, and some companies commit such activ-
ities. For instance, 19 review management companies were caught and fined because of
injecting dishonest ratings into various sites, such as Yelp.com (Sved 2014).
In the cyber world, there are two types of measures for rating: non-computational
and computational (Zacharia et al. 2000). A non-computational rating is not a numer-
ical value; instead, it keeps a record of all the activities associated with that evaluation.
A famous example of a non-computational rating system is the Better Business Bureau
Online, whose primary responsibilities are handling disputes and tracking complaints
(Azari et al. 2003). In contrast, a computational rating is calculated based on the evalu-
ations from all evaluators. For the computational rating, there are two types of rating
systems: content-and user-driven. Content-driven systems (i.e., WikiTrust) use auto-
mated content analysis to derive ratings by comparing contributed content with the
truth. The content is more reliable if it is less frequently modified. However, there are
several limitations to content-driven rating systems. The ratings are calculated auto-
matically and are nontransparent to users; therefore, the users’belief in rating scores is
affected as they do not understand the internal calculation process. Additionally, such
systems rely on raters’proactive content verification. If users do not provide feedback,
rating reliability becomes misleading.
User-driven ratings systems (i.e., eBay or Amazon) compute their rating scores based
on users’rating. In user-driven rating systems, the rating score can be calculated either
as the difference between all positive and negative scores (e.g., eBay) (Resnick and
Zeckhauser 2002), or as the average of all ratings (e.g., Amazon) (Schneider et al.
2000). In a more advanced version, it can utilize the previous positive and negative
ratings as parameters to formulate the beta probability density function of each rating.
For example, in beta reputation systems, given the previous rating score and the new
rating, the updated one can be calculated (Jøsang and Ismail 2002).
In user-driven rating systems, dishonest raters may have different goals; thus, they
behave differently. For example, the incentives of fraudulent raters can either be
promoting their own product or demoting their competitors’. According to Dellarocas
(2000), unfairly high ratings injected by fraudulent raters to a target entity are termed
as “ballot stuffing,”and the unfairly low ratings are called “bad mouthing.”
Cai and Zhu Financial Innovation (2016) 2:20 Page 3 of 10
Regardless of bad mouthing or ballot stuffing, we follow six types of fraudulent raters’
behavior models summarized in Irissappane et al. (2012). The first three are “constant
attack,”“camouflage attack,”and “whitewashing attack.”A“constant attack”indicates
that a fraudulent rater consistently provides unfairly high (low) ratings to the target
entity in a ballot stuffing (bad mouthing) scheme. A “camouflage attack”is performed
by a strategical fraudulent rater, who will inject fair ratings to non-target entities to
camouflage himself/herself, in addition to injecting unfair ratings to the target entities.
This type of attack brings more challenges to the reliability of reputation systems, as it
is more difficult to differentiate fraudulent raters from honest ones because their
ratings are more similar to each other. In a “whitewashing attack,”a rater will inject
unfair ratings to the target entity for a period. Subsequently, he/she whitewashes
himself/herself by creating a new account and thereupon behaving as an honest rater.
Each of these three types of attacks can be used to commit either a ballot stuffing or
bad mouthing scheme, with the only difference being the rating value (i.e., unfairly high
or unfairly low) of the target entity. In addition to the abovementioned attack types,
Douceur (2002) proposes the concept of the “Sybil Attack.”Different from the previous
three types of attack models, it does not regulate the fraudulent rater’s behavior, but
describes the overall fraudulent population. When there are more dishonest raters than
honest ones in the system, the system is under a sybil attack. This can be combined
with the three types of fraudulent behaviors, namely constant, camouflage, and
whitewashing, resulting in a “sybil constant attack,”“sybil camouflage attack,”or a
“sybil whitewashing attack,”respectively, wherein each one indicates that in the
reputation system, there are fewer honest raters than fraudulent ones.
An overview of blockchain technology
Blockchain is built on the Bitcoin protocol, the first peer-to-peer (P2P) electronic case
systems that allow payments to be sent online from one entity to another without the
intervention of a financial institution (Nakamoto 2008). As a result, trust is established
not by powerful intermediaries, such as banks, governments, and technology compan-
ies, but through mass collaboration and clever code on the Blockchain (Tapscott and
Tapscott 2016). Blockchain is a transaction database shared by anyone participating in
the system. With cryptocurrency, transactions records are stored as data blocks, which
are chained together cryptographically. It is open to any node in the system and
everyone can enter new entries. However, new blocks cannot be added without the
proof-of-work and agreement by the other nodes participating in the system.
Hereby, blockchain guarantees the accuracy of the information it stores. Blockchain
is immutable; therefore, once a block is modified, it will also regenerate every
subsequent block (Khan 2015).
The mechanism of blockchain technology can be explained from its first application.
Bitcoin, a form of digital cryptocurrency, is different from the traditional currency
issued by governmental financial institutions. Bitcoin is a ledger, storing account
information and their balance, which works as an online bank account that every user
can access, receive, and transfer money. This is different from a traditional bank,
wherein the information is controlled by the central authority. The ledger of Bitcoin is
owned by everyone in the Bitcoin network.
Cai and Zhu Financial Innovation (2016) 2:20 Page 4 of 10
While the information is open to any code, information security is guaranteed by
using one half of a digital signature (Elliptic Curve Digital Signature Algorithm). Each
owner of the account holds and keeps half of the digital signature to himself/herself,
which is called a “private key”; the other half of the digital signature, which is called a
“public key,”is published to all participants in the network. Each account owner can
send bitcoins to the public key and use it to verify the accuracy of the signature;
however, only the owner with the private key can use the bitcoins from the account. To
send bitcoins, an account owner broadcasts the “sending-money”notification so that
all participants in the network are notified; then the network validates the account
information against its key. As the account balance will go down, the account owner
needs to ensure that the account has sufficient balance; subsequently, a transaction will
be made with the receiver’s account and the balance will be adjusted accordingly. All
nodes in the Bitcoin network will be notified about this transaction, and each node will
include it and pass it on to other nodes. Once a transaction is included in a block, it
becomes certified. Finally, every node on the Bitcoin network will have the same copy
of the entire ledger. Therefore, instead of using a bank’s network, a group of computers
keeps a ledger.
In traditional banking systems, each customer is authorized solely to his/her own
account information by using a pair of user name and password; however, in the
Bitcoin network, every account is kept as a copy in each node. Therefore, the Bitcoin
network needs to ensure that each transaction update is authentic; as a result, the
digital signature is required for every transaction (Driscoll 2013). Whenever an account
owner needs to initiate a transaction, he/she needs his/her private key to sign the
transaction. Other participants can use the public key to verify the validity of this new
transaction. If authentication is completed without any problems, a new digital signa-
ture will be added to the Bitcoin transaction, which can be completed only by its new
owner. Other participants work independently on their own copy of the blockchain so
as to ensure that the digital signature is incorruptible and the sender account has suffi-
cient balance. A verified record is added to the block and is irreversible (Peck 2015).
Therefore, in essence, the blockchain is a recordkeeping technology, where each
transaction is interlinked with an earlier record in the chain. This arrangement only
converges if all participants agree on what should be the most recent version of the
blockchain (Peck 2015). As it requires group consensus, the process to add new trans-
actions into the blockchain is both complex and costly. A large amount of computation,
which uses hash functions, is required from every node to verify and accept the new
record. Thus, once the transaction is included, it is verified and not easily changed.
Thus, transactions in the blockchain network seldom go backwards. Transactions
entered into the blockchain ledger are secure; as such, they are described as permanent,
incorruptible, and irreversible records (Khan 2015).
Blockchain system can be applied in various problematic domains. As all transactions
must be publicly broadcasted and permanent, it can provide various types of services,
such as delivery verification in the supply chain industry, degree verification in the
educational industry, money transfer security in the financial industry, and payment
chargeback risks mitigation in e-commerce (Khan 2015). Another important applica-
tion area for blockchain systems is financial fraud detection. To facilitate business
decision making, a variety of systems have been developed for processing applications.
Cai and Zhu Financial Innovation (2016) 2:20 Page 5 of 10
Given the information provided by users, a decision is made based on the built-in
decision rules. Such systems significantly improve the effectiveness and efficiency of
application decision making, although they are vulnerable to manipulated input
information, such as loan fraud.
For example, a decision on a loan application can be generated based on inputs of
customers’personal information. When a user intends to apply for a loan through an
online application system, he/she may falsify some of the personal financial informa-
tion, such as a fake repayment history, thus increasing the possibility of acceptance.
Consequently, financial institutions have suffered tremendous losses due to loan fraud
(Kim et al. 2012). As blockchain systems can keep historical transactions records,
applicants cannot falsify information to obtain a favorable decision. Among all the
application areas, we focus on the applications on rating fraud detection in the
subsequent section.
Effectiveness of blockchain on rating fraud
Recently, scholars have been focusing on redesigning reputation systems in the era of
blockchain technology. For instance, Vandervort (2014) discusses the feasibility and
challenges of designing the bitcoin-based reputation systems. As privacy is an import-
ant concern for users who are reluctant to provide information, Schaub et al. (2016)
propose how to utilize digital signatures to design reputation systems that can protect
users’privacy. In a similar vein, Soska and Christin (2015) propose a system “Beaver,”
which protects users’privacy, while being resistant against sybil attacks by charging
fees. Dennis and Owenson (2016) design reputation systems with underlying
blockchain technology. These systems generate and broadcast a binary P2P rating on
receiving the correct file.
As discussed in Background section, privacy concerns drive users to contribute
fraudulent information. With support from blockchain technology, Schaub et al. (2016)
propose that customers and sellers use private and public keys to communicate with
each other. Customers can be assigned tokens from sellers to be allowed to provide
feedback. However, the rating can be unlined from customers. Therefore, customers do
not need to worry about retaliation, and can provide real feedback.
In addition to privacy concerns, another important reason for rating fraud is seeking
inappropriate profits. In the financial application fraud discussed in An overview of
blockchain technology section, the fraudulent information is objective in that it is fact-
based and provable. Thus, the ground truth of the fraudulent information can be
assessed. As regards rating fraud, if it occurs in non-computational reputation systems
and content-driven reputation systems, since the rating information is also fact-based,
blockchain systems can be utilized to verify the validity of claims and content. However,
for the rating fraud in user-driven reputation systems, the information is subjective in
that it lacks ground truth. For example, even if an attacker demotes a decent item by
injecting a poor rating, he/she can always insist that it is based on difference in individ-
ual preference. Therefore, even with accurate historical transactional records, it is still
difficult to detect fraud on subjective information. In the method developed by Dennis
and Owenson (2016), they propose that human opinions are removed from reputation
systems. Instead, the reputation is represented by a binary value, which reflects if the
file is received by the users. In this case, the systems contain only objective information,
Cai and Zhu Financial Innovation (2016) 2:20 Page 6 of 10
which is fact-based. As such, blockchain technology can be used to support fraud
detection. However, the purpose of reputation systems is to help users better under-
stand sellers. If we only record whether the requested product is delivered, it does not
satisfy all customers’needs. Product delivery is an important aspect of a seller; however,
there are many other factors, such as product quality, which are also very important to
customers’purchasing decisions.
Other studies on blockchain-based reputation systems, such as Soska and Christin
(2015), propose a preventative mechanism against subjective information fraud, which
is increasing the fees of injecting ratings. Such a preventive strategy has already been
proposed in rating fraud for traditional reputation systems. For example, we can bind
each account to one unique IP address to prevent a sybil attack (Douceur 2002).
SybilGuard, a protocol proposed by Yu et al. (2006) is designed to increase the difficulty
of controlling multiple accounts to perform the attack. In a similar vein, Epinions.com
encourages raters to provide honest feedback by sharing income with them (Jøsang and
Ismail 2002). The preventive mechanism increases the costs of fraud, so that it can
mitigate sybil attacks. However, they are not effective if the perceived benefit from
attacks is greater than the cost.
In traditional reputation systems, such as Amazon.com and Expedia.com, rating fraud
can be dealt with by using the label “verified transaction.”For example, Expedia raters
must be real customers, i.e., who have checked in a hotel for at least one night (Mayzlin
et al. 2014). Thus, ratings on Expedia are claimed as “verified ratings.”Similarly,
Amazon.com labels the rating if it is from a “verified purchase.”With the support of
blockchain technology, it is much easier to identify if the rating is from a valid pur-
chase. Therefore, in blockchain-based reputation systems, only verified transactions
and their associated ratings will be stored, making “verified”labels no longer necessary.
The immutable transactional records in blockchain-based reputation systems can be
used to prevent some types of rating fraud. Schaub et al. (2016) suggest that bad
mouthing, including sybil and non-sybil attacks, can be mitigated if a user can only rate
a product after receiving a token from the seller. In such a scenario, every submitted
rating must come from a transaction. Limiting ratings only to those with valid transac-
tions significantly decreases the motivation of bad mouthing. This means that, if one
company intends to demote its competitor’s product, it first needs to contribute toward
the competitor’s sales. However, this is unlikely under a sybil attack, wherein more than
half of the transactions are completed by fraudulent customers assigned by the
competitors. Although it cannot rule out bad mouthing completely, if the perceived
benefit is greater than the cost, a company needs strategically analyze how many
resources it should devote to the fraud.
The effectiveness of blockchain-based reputation systems may be limited in ballot
stuffing sybil attacks. The seller is likely to promote his/her own product by encour-
aging fraudulent raters to complete real transactions. Raters may be offered free or
significantly discounted products so as to inject a positive review. This phenomenon
has already been noticed by Amazon.com. Amazon has removed “verified purchase”
badges from reviews associated with discounted transactions (Coleman 2016). The
blockchain-based reputation systems can reflect such discounted transactions accur-
ately, but are less effective in stopping their occurrence. Furthermore, sellers can allow
customers to first pay the full amount, submit ratings, and pay them back in other
Cai and Zhu Financial Innovation (2016) 2:20 Page 7 of 10
ways. Although transaction records are incorruptible in the blockchain-based reputa-
tion systems, the fraudulent raters in such false “real transactions”are not detected.
A strategy proposed by Schaub et al. (2016) to prevent ballot stuffing is limiting the
total number of tokens for each seller. Therefore, if a seller gives tokens to fraudulent
ratings, it will reduce the number of real transactions. This strategy is effective as it can
result in a tradeoff between rating and profit for the seller. The assumption underlying
this strategy is that the total size of ratings is limited. The purpose of reputation
systems is to encourage users to provide feedback, and there is a natural difference
between the rates of submission of products’ratings, e.g., “hit”products can receive
more feedback within a shorter period, while unpopular products may not be commen-
ted on by customers for a long time. Consequently, it may not be feasible to limit the
number of ratings that can be received from the start.
As regards ballot stuffing, constant and camouflage attacks, such subjective informa-
tion fraud can be mitigated; although, it is difficult to prevent or detect them in
blockchain-based reputation systems due to the existence of false “real transactions.”
However, blockchain technology can be used against whitewashing attacks. In the
blockchain-based reputation systems, the user account can be created with real
identity, while the real identity is not disclosed. Therefore, once a rater has injected
fraudulent subjective information, he/she can leave the system, but he/she cannot
create a new account so as to whitewash his/her past rating history.
Conclusions and discussion
Interactions in the cyber world are characterized by anonymity, which can occur be-
tween people who do not know each other’s real identity. However, it may be risky to
interact with unfamiliar items or unknown sellers in the cyber world. Rating systems
have been shown to be effective for customers to pre-evaluate the quality of the object
and control interaction-specific risks. However, rating systems are vulnerable to rating
fraud, which may mislead the customers’purchasing decisions and further affect their
motivation for future interaction. Blockchain is a distributed public ledger, which keeps
records on thousands of computers. All records stored in the system are entered with
proof-of-work, based on group consensus, and cannot be tampered with. Therefore,
the true records in blockchain systems can be used to address the integrity issue.
This study discusses the potential strengths and limitations of blockchain-based repu-
tation systems under rating fraud. Blockchain systems are very effective in preventing
objective information fraud, such as loan application fraud, where fraudulent informa-
tion is fact-based. However, for subjective information fraud, such as rating fraud,
where the fraudulent information is not easily verified, blockchain systems are not
effective in all scenarios. On one hand, blockchain technology is effective in preserving
customers’privacy. Users may be reluctant to provide true information to reputation
systems because of personal privacy concerns. As such, blockchain systems can prevent
fraudulent ratings submitted by such users, as their real identify will not be disclosed.
On the other hand, users may inject fake information into systems to promote
their own products or demote their competitors’products. Blockchain systems are
effective in preventing some types of rating fraud, such as bad mouthing and
whitewashing attacks, but they may be unable to prevent ballot stuffing sybil,
constant, and camouflage attacks.
Cai and Zhu Financial Innovation (2016) 2:20 Page 8 of 10
The limitation that ratings can only be submitted after completing transactions
increases the cost of rating fraud. However, sellers can still enter into agreements with
raters for incentivized ratings. Fraudulent raters can submit unfair higher ratings in
exchange for significantly discounted products or services, or they can complete the
transaction, submit the rating, and be subsequently reimbursed by the seller. In such
cases, blockchain systems keep accurate transactional information; however, they
cannot verify whether the ratings are fraudulent or not as they are based on individual
subjective evaluation. Additionally, we should be aware that blockchain systems are not
perfect regarding information security. Although it is a recordkeeping technology that
stores permanent and incorruptible records, it may not always guarantee the reliability.
For example, blockchain systems can be utilized by some sophisticated hackers to inject
malicious nodes and spread viruses. As all computers keep the same copy, a larger
number of computers will be infected. In addition, Lemieux (2016) discusses practical
issues for record reliability in blockchain-based solutions.
However, as users’privacy can be protected in blockchain systems, we can only allow
accounts created using real identities to submit ratings. Compared to the traditional
reputation systems wherein one person can control multiple account IDs and inject
fake ratings, blockchain reputation systems can significantly decrease the number of
fraudulent ratings. Future research lie in deep understanding of blockchain technology
and development of new technologies in detecting both objective as well as subjective
information frauds.
Acknowledgements
We would like to thank the anonymous reviewers for their constructive suggestions and comments that help to
improve this manuscript.
Author’s contributions
Both authors developed the central idea and contributed to the conceptualization of the study. All authors read and
approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Zicklin School of Business, Baruch College, City University of New York, New York, NY, USA.
2
College of Business, Iowa
State University, Ames, IA, USA.
Received: 12 November 2016 Accepted: 24 November 2016
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