The Role of Trust Systems in eCommerce

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The Role of Trust Systems in E-Commerce
Jessica Van Meter
Trust is an important and misunderstood concept that extends its role from the general to the
situations specific to e-commerce. Electronic commerce is a growing business field in which the
standard formations of trust appraisal are not widely available. As a result, gaining trust of
consumers is an important problem which is sought to be solved by investigating psychological
and marketing vocabulary, while building mathematical models for assigning values based on
reputation. If a high reputation can be demonstrated, an increase in sales will result. However,
there is the unintended, possibly subconscious result that the higher the reputation, the more
likely an online seller is to take a particular transaction and its success for granted. More precise
measurements for trust and reputation are needed, given these drawbacks to the current models.
However, much work has been done, and this is a survey of the ongoing research covering trust
in e-commerce.

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Trust can be taken for granted, perhaps because its definition is also taken for granted and
misinterpreted as the expectation that someone will do something that is desired. With the help of
psychology and business marketing it is helpful to identify the key features that define trust and
how it is gained or lost. Here, trust is the expectation that the trusted party will fulfill a beneficial
act, given a position of vulnerability on the part of the trusting party (Koehn 2003). This can be
seen in the form of an agreement that the trusted party will perform such an act.
Such an agreement can be either implicit or explicit. For example, if my car is started for
me each morning by a family member, I can develop an expectation based on the implicit routine
followed by this family member. If my family member fails to do so, unexpectedly or without
prior notice, it will be more difficult that I will trust that family member to do so tomorrow
morning. However, there is an additional element when considering trust in a party, and not just
their actions. Whether or not the infraction of expectations occurs, trust is mostly affected on
whether the trusting party sees the breach as competing with the ability to thrive (Koehn 2003),
whether in life or in business. My general trust in my family member would not be completely
severed by their failure to act, because it will not impact my ability to thrive.
Additionally, trust is different from a trusting attitude, nor is trust dependent on the
formation of a relationship. One can trust without being a particularly trusting person. A trusting
attitude is described as a middle point between a paranoid personality and a carefree or naive
attitude. Trust is required to form a relationship with an unknown party, and therefore cannot be
required (Koehn 2003).
In Computer Science, trust needs to occur in various places and in varying degrees, in
order for business and development to occur. These places include, but are not exhausted by, chat

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room conversations, message board postings, online journalism, downloading of safe files,
research, e-voting systems, and online purchases. This paper will focus on the effects of trust on
online purchases, as this is a growing marketplace. Additionally, trust can be difficult to foster in
an electronic arena, because the usual queues that are used to assess a party's trustworthiness are
not present. Often the party is not known personally, or locally. Physical traits and behaviors
cannot be observed, and regularly expected exchanges like handshakes cannot be performed.
Furthermore, what makes trust in an online purchasing context particularly interesting is that it
comes with a higher risk than other arenas, like message board posting, which is usually
mediated by a third party and does not require the risk of one's financial information.
Already in 2000, David Gefen reported that Amazon, a single online seller which at the
time did not feature independent sellers, had 6.2 million customers, worldwide. E-commerce
created a smaller globe in which all countries now contain many prospective customers with
different cultural expectations for forming trust. The Better Business Bureau recognized the
importance of fostering “trust and confidence” in online business (Gefen 2000).
There are four identified types of trust which vary in their formation and their depth.
These include goal-based trust, calculative trust, knowledge-based trust, and respect-based trust.
Goal-based trust is the simplest to form, because it is defined by both parties having the same
end result in their interests (Koehn 2003). As such, it is also the most superficial kind of trust,
and once the common goal disbands or seems unlikely, there is a risk a party will terminate the
relationship or change behavior beyond the predictable or beneficial.
Calculative and knowledge-based trust are similar, in that information is used to produce
predictions about the expected behavior of the other party. Calculative trust uses information

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gathered through means outside a personal interaction or knowledge of the party. In this form of
trust, reputation and word of mouth provide a lot of weight in the balancing of risks and benefits
of trusting the party. In contrast, knowledge-based trust uses information the trusting party
gathers based on prior interactions with the party to be trusted (Koehn 2003). So, calculative
trust is like survey research whereas knowledge-based trust is like empirical experience.
Respect-based trust is that which is formed through a close relationship. Relationships are
not necessary for general trust, but for respect-based trust, an intimate knowledge and
commonality of the party's behaviors, habits, interests, and goals is used to form a much deeper
and more impervious form of trust. Because the party is so well known, failure to fulfill
beneficial expectations is more easily understood and thus forgiven. This form of trust is the
form that can get the closest to knowing the motivations of the trusted party, making trust less
volatile. Thorough and customized communication adds to the strengths found in respect-based
trust
Returning to the family example, I will likely not fault my family member for not
upholding their routine expectation if I know their work schedule does not permit them to start
my car that day, or if I am aware of a trying time they are experiencing. I may even be able to
infer a reason for their behavior, based on our history, which will give the arrangement time
remedy itself. I do not need to wonder if they no longer wish to be of assistance to me in the
mornings, or second guess their motivations in any other way. It can be expected that if there
were a reason for our arrangement to terminate, it will be communicated, and most likely with
reasons to justify the termination. For these reasons, it makes sense that respect-based trust can
be the most difficult to gain on an online business platform, but the benefits make it the most

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desirable.
There are several factors affecting the formation of trust. Of these, disposition to trust is
the greatest influence, and also the most difficult to change. Disposition to trust is the way in
which a person is hardwired toward the concept and practice of trusting and is not specific to any
situation(s). This is decided by personality, and also life experience. However, this life
experience is of the general form, and not related to any of the particular details of the situation
in which trust is needed (Gefen 2000).
In contrast to disposition, the easiest way to affect the formation of trust is through
familiarity. Familiarity is the use of past experience to infer reasoning behind a party's actions. It
serves to reduce uncertainty in a trust-based situation by providing structure. One of the most
common areas in online commerce for familiarity to have a profound role, is in the user
interface. Using previous experience, a user has an understanding of how an interface works and
completes activities based on that understanding (Gefen 2000).
Familiarity and trust are two separate concepts, but are closely related. If fact, they are
complimentary in their goal to reduce uncertain factors when making decisions about the risks
and benefits involved with a particular entity performing a specific action (Gefen 2000).
Familiarity concerns the present context, whereas trust is an effort to appraise the future context
and results. Additionally, familiarity produces an understanding, while trust produces beliefs or
expectations (Gefen 2000). For example, familiarity is when a person knows a particular website,
and maybe how it runs, its reputation or what it does. Trust is when a person uses a part of the
website in which the assumption of risk is necessary, with the expectation that it will be a
beneficial experience. More specifically, trust is when a person enters their banking information

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into a website with the expectation that it will be used to make the desired purchase, and not for
other unauthorized uses.
Given the definition and function of familiarity in forming trust, it should take a large role
in the development of User Interfaces (UI). In order to build trust and form a relationship with a
user, an interface should be easy to use and with consistent form across releases. Dramatic
changes will cause a lack of trust, perhaps until the user reorientates to the location of software
features, or perhaps not at all. This could possibly explain the extreme backlash against the
newest form of the Windows operating system, given that the well known layout of multiple
decades has rearranged to location of many key features, like where the user should click to shut
down their machine.
Linus Torvalds recognizes the important role that UI's can take, when he says “In many
cases, the user interface to a program is the most important part for a commercial company:
whether the program works correctly or not seems to be secondary” (Torvalds via Yamagata
1997). In the interview in which Torvalds makes this relatively controversial claim, he is
speaking to why programs like word processors are generally easier to use in commercial
software programs like word processors, compared to those available on Linux platforms. He
makes the distinction of skills between free software and commercial software, and adds that
commercial software can produce the finishing touches on a UI that makes it more accessible to
the everyday user, while free software has a long history of being quite effective in technical
roles. This distinction lends to the conversation on trust, given that technical workers already
have formed trust in Linux and other free software platforms through schooling and experience.
Everyday users do not have this relationship with software, so commercial companies like

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Microsoft have to work harder to gain their respect.
As previously mentioned, consumers use evidence such as a seller's reputation to decide
if that seller is trustworthy. Reputation represents the way the seller is perceived by the public
and helps consumers decide if a business is worthy of trust. Online trust and reputation systems
help users decide if a business has a good reputation or is trustworthy by assigning a numerical
value from a scale representing the range of trust an online seller has prior to the transaction
which is to be made (Josang et al 2008).
The Bayesian models represent an aggregate of consumer feedback and is a popular
model for online auction sites like eBay, Allegro, and others. This calculation is not a simple
average, and accounts for the following variables: the total set of sellers on the auction site, the
total set of buyers, the set of past transactions the seller has, the number of past transactions a
specific seller has before a new transaction occurs, and the time when a transaction happens (Liu
et al 2012). The model has two common variations which can produce either a binomial value or
a multinomial value.
Binomial Bayesian values take ratings from a scale from good/positive/satisfactory to
bad/unsatisfactory. These models come with the added risk that the pool of reviewing consumers
could become polarized. This means that half of the audience will find the seller favorable and
trustworthy, while the other half does not. This of course is not a helpful outcome, as the
uncertainty is unchanged. There is an equally perceived chance that the seller will act favorably
or not (Josang et al 2008).
Multinomial Bayesian models take a wider range of reviews, thus giving a more precise
outcome. These can be seen most commonly as a choice between 0 or 1 and 5 stars, with 5 being

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the best possible experience (Josang et al 2008). This removes the possibility of polarized ratings
but there is still one area where the Bayesian models fall short is they neglect the fraction of
negative transactions that occur with sellers with a high reputation rating.
This overlooked phenomenon is called imprudence, and it has four key details. These
include the “reputation value, the number of past transactions, the time difference between the
first transaction and the current one and the item price” (Liu et al 2012). Combined, these factors
can cause carelessness with a seller with a high rating, and if the price of the item is low enough,
the attention to detail that perhaps once rose the seller's ratings to the top are less important. This
is given that the seller knows, whether subconsciously or consciously, that a single poor rating
will not change the high reputation value. However, this phenomenon is only true with sellers
that have large data sets, and the Bayesian modes do serve to be reliable for sellers that have a
modest number of transactions under their belts. By additionally considering the problem of
imprudence, these models can be increased in their effectiveness for sellers with any size of data
set.
As online marketplaces continue to grow in size they are now competing with physical
market places. The systems used to appraise the trustworthiness of electronic sellers continues to
become more relevant and relied upon as factors of trust such as familiarity, reputation and
imprudence are identified and measured on a more precise scale. As such, trust and reputation
systems is also a growing field of research with many exciting theories about how to decide
whether or not someone we cannot meet in person is worth our investment of risk in online
business climates.

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References
Fink, R. A., Sherman, A. T., and Carback, R. Tpm meets dre: reducing the trust base for
electronic voting using trusted platform modules. Information Forensics and Security,
IEE Transactions on 4, 4 (2009), 628 – 637.
Gefen, D., Karahanna, E., and Straub, D. W. Inexperience and experience with online stores: the
importance of tam and trust. Engineering Management, IEEE Transactions on 50, 3
(2003), 07-321.
Gefen, D. E-commerce: the role of familiarity and trust. Omega 28, 6 (2000), 725-737.
Josang, A., Bhuiyan, T., Xu, Y., and Cox, C. Combining trust and reputation management for
web-based services. In Trust, Privacy and Security in Digital Business. Springer, 2008,
pp. 90-99.
Koehn, D. The nature of and conditions for online trust. Journal of Business Ethics 43, 1-2
(2003), 3-19.
Liu, X. Datta, A., Fang, H., and Zhang, J. Detecting imprudence of reliable sellers in online
auction sites. In Trust, Security and Privacy in Computing and Communications
(TrustCom), 2012 IEEE 11th International Conference on(2012), IEEE, pp. 246-253.
Udo, G. J. Privacy and security concerns as major barriers for e- commerce: a survey
study. Information Management & Computer Security 9, 4 (2001), 165-174
Yamagata, H. The Pragmatist of Free Software: Linus Torvalds Interview. hotWIRED Japan.
1997. Web. 20 Nov. 2014. http://tech-insider.org/linux/research/1997/0920.html

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