International Journal of Scientific & Engineering Research Volume 3, Issue 4, April-2012 1

ISSN 2229-5518

Trust Vs Complexity of E-Commerce Sites

Devendera Agarwal, R.P.Agarwal, J.B.Singh, S.P.Tripathi

Abstract— E-Commerce suffers from uncertainty which can produce devastating results. The user first checks the level of security and then proceeds further. At the same time the user switches to another e-commerce site if he has to deal with several layers of security. To overcome this drawback e-commerce sites are now finding a solution of maintaining high security (Trust) with lesser complexity as far as possible. Our paper focuses on the issue of development of a framework to provide an optimal relationship between the two.

Index Terms— Complexity, Threat to e-commerce, Fuzzy Rule, Security, Tradeoff, Transaction, Trust.

—————————— ——————————


NDIA today is facing with various kinds of threat to e- commerce systems. The problem arises when we increase the security of the e-commerce website, the complexity at the user level also increases, which in turn affects the volume of sale. While traditional marketing does not involve any type of complexity since the consumer deals directly with the sup- plier. Since internet marketing does not involve any face to face direct interaction so a visual interface is essential. There are various types of online buying behavior models like Bett- man (1979) and Booms (1981) in which the focus was on per- sonal characteristics viz. Culture, Social Group and Physiolog- ical Behavior. Lewis and Lewis (1997) identified five different
types of web which remain valid today:
Directed information-seekers: These users will be look- ing for product information and are not typically planning to buy online.
Undirected information-seekers: These are the users,
usually referred to as 'surfers', who like to browse and
change sites by following hyperlinks. Members of this
group tend to be novice users and may also click banners
of the website.
Directed buyers: These buyers are online to purchase spe-
cific products online. For such users, brokers that compare
product features and prices will be important locations to
Bargain hunters: These users want to find the offers
available from sales promotions such as free samples or
Entertainment seekers: These are users looking to interact
with the Web for enjoyment through entering contests
such as quizzes, puzzles or interactive multi-player
Under all the above categories the main focus is the trust of web users [24], [25] which will finally lead to purchase. The communication between server and client are not secure un-


Devendera Agarwal is currently pursuing his Ph.D. from Shobhit Univer- sity, Meerut, India. E-mail:

Prof. R.P.Agarwal & Prof. J.B.Singh, Shobhit University, Meerut, India,


Prof. S.P. Tripathi, Department of Computer Science, I.E.T, Lucnkow,

India, E-mail:

less it is providing a safe and secure transaction. To reduce the risk we have to deal with development of trustworthiness of the web services, which finally means increasing the complexi- ty of the website.




Fig. 1. Trust/ Complexity Matrix.

From the above figure we can conclude that there has to be some situation in which a trade off between Trust Level and Complexity of the transaction has to be maintained. This trade off can be achieved by the help of development of Fuzzy Rule base, but simple Fuzzy Rule base will not be sufficient for this purpose, so we extend this problem and solve it using Evolu- tionary Multi-objective Optimism [9], [10], [12].


The work by H. Ishibuchi & H. Tanaka (1994) highlights the construction of Fuzzy Classification of various entities using genetic Algorithms. Later on they extended their work (1995) using If-Then-Else rules. M. Setnes (1998) developed a Rule- Based system for developing the Precision & Transparency. D. Nauck (1999) worked on the interpretability aspect of Medical Data; we are motivated by their work and extending it for e- commerce websites. Y. Jin (2000) developed a framework for modeling high dimensional system in finding out their Com- plexity and Interpretability aspect. L.Castillo (2001) developed the best rule in a genetic fuzzy learning algorithm. M.Setnes (2000) also developed a mechanism of GA-based Modeling & Classification which measures the Complexity and Perfor-

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International Journal of Scientific & Engineering Research Volume 3, Issue 4, April-2012 2

ISSN 2229-5518

mance of the system.


Genetic Algorithms [6], [8], [17] have been frequently used to model a solution for conflicting goals. Let Trust (T) be a measure of security which the customer will be provided and Inverse of Complexity (C) be the user comfort level. Applying the Fuzzy Rule base we can get
Maximize Trust (T) (1)
But it leads to compromise in the complexity (C) of fuzzy rule based systems [2], [3], [5], [7]. According to consumers survey most of consumers in India considers Trust and Ease of Use (Lower level of Complexity) at the same time. The above problem can be formulated as
Maximize Trust (T) subject to
Inverse complexity (C) (2)
where complexity (C) is the measure of fuzzy rule system.
We can develop a single objective function to the above so- lution given as:
Maximize ƒ(Trust (T),
Inverse of Complexity (C)) (3)
We can also use weights in order to determine the exact function for e-commerce site.
Maximize (w1) Trust (T) +
(w2) Inverse of Complexity (C) (4)
We proceed with development of more refined stages in which we can focus on various stages of membership func- tions. Consider a simple single output function y = f(x) an ap- plication of Takagi-Sugeno method [7], [11], [15] we can write it as:
Rule Ri : if x is Ai then y=ai+bjx, i=1,2,…N Rule Rk : if x is Ak then y=ak+bkx, k=1,2,…N
Rule Rz : if x is Az then y=az+bzx, z=1,2,…N (5)
From the input-output data we can derive the relationship between Trust and Complexity of the e-commerce site consi- dering three Sugeno Rules.
We develop a heuristic rule[1], [2] denoted by three lines A, B and C as the subsequent of the linear function with fuzzy sets A1, A2 and A3. Each of the Fuzzy Rule can be represented in triangular Fuzzy Sets.

Rule R1: If TRUST is SMALL and COMPLEXITY is HIGH Then User’s Ease of Use is MEDIUM.

Rule R2: If TRUST is LARGE and COMPLEXITY is ME- DIUM Then Users Ease of Use is HIGH

Rule R3: If TRUST is SMALL and COMPLEXITY is SMALL Then Users Ease of Use is HIGH


Fig. 2. Three Takagi-Sukeno Rules

Based on the above rules we try to develop a plot between Complexity and Trust and develop our interpretable solution [13], [14], 16], [18], [19] between the two entities.










This output value is given as:



0 0.2 0.4 0.6 0.8 1 1.2 1.4


(ai bx )Ai ( x)

y( x)  i 1

Ai ( x)

i 1


Fig. 3. Input Output Data using Fuzzy Data Set

Possibly we can also merge the above set of rules to achieve more refined results, but a relationship generated by optimiza-
where y(x) is the estimated output value for the input value x
and µAi (x) is the membership value of the antecedent fuzzy set Ai.
tion rules gives some gridlines in the area of relationship be- tween the two entities.

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International Journal of Scientific & Engineering Research Volume 3, Issue 4, April-2012 3

ISSN 2229-5518


It is very difficult to interpret the exact relationship be- tween the two entities. Different Fuzzy rule are being applied in order to determine the appropriate interpretability. The method that we have used is the application of Fuzzy Optimi- zation Theory [20], [21], [22], [23] to find the probable relation- ship between Complexity and Trust. The future extension would be to use Evolutionary Algorithm [4], [5] in finding out the best possible trade-off between the two entities.


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