modelling e-commerce systems’ quality with belief networks

8/3/2019 Modelling E-Commerce Systems Quality with Belief Networks

1/6

VtCIMS 2003 lntemational SymposiumonVinual Envirom,cnts. Hum-Computer Intrrfaco. and MrarurrmcntSyuemsLqann . Switzrrland. 27-29July 2003

Modelling E-Commerce Systems Quality with Belief NetworksAntonia StefaniSchool of Science and Technology

Hellenic ope n University16 SachtnuriStr., atra,GR26222, GreecePhone: +30 2610 362586Fax: +30 26 10 361410E-mail: [email protected]

Michalis Xenos School of Sciencean d TechnologyHellenic Open University16 Sachtnuri Str.,Patra,GR26222, GreecePhone: +30 2610 361485Fax: +30 2610 362349E-mail: [email protected]

Dimitris StavrinoudisComputer Engineering andInformatics DepartmentPatras University,Rion, GR26500, GreecePhone: +30 2610 362556Fax:+302610361410E-mail: [email protected]

A -This pnper focuses on qunlily aspects of e -comr cesystems nnd proposes n method fo r modelling such system basedon Belief Networks. The pnper discusses fhe Iheordicnlbnckground of the proposed model, aswellprneriecll issues nrisingfrom its npplicntion. The basic norotion and concept of BeliefNetworks is briem presenle4 while enphnsis is plnced on lhemodels stmdure nnd its usage. The presented model cnn beutilised or assessingIhe qunli&of e-commerce s yslem, aswell asfo r aiding in qunlily nssurnnce during the design nnd developmentphnse of such syslem

1. INTRODUCTIONE-commerce is a constantly expanding field. This fact isconfirmed by the increasing number of enterprises that investinto the creation of e-commerce systems and the continuousexpansion of economic and commercial transactions throughthe Internet. E-commerce can be defmed as follows [I] :sharing business information, maintaining businessrelationships and conducting business transactions by the

means of telecommunication networks. Depending on thetype of transactions performed electronically, there are twobasic categories of e-commerce systems [2]: Business toConsu mer (B2C) and Business-to-Business (B2B).In E-commerce systems, interaction with the end-user isconducted through web-based applications including ahuman-computer interface. Since all user-systemcommunication is realized based on such interface, it is selfevident that the quality of an E-commerce system is directlyrelated to the quality of the human-computer interfacethrough which the end-user interacts with the web-basedapplications. Usually, end-users value E-commerce systemsthat are flexible, usable, easily adaptable to their needs andthat offer a full range of applications. But how can oneevaluate E -commerce systems and define the extent to whichthey meet end-users requirements? To this end, it isnecessary to define what constitutes a high-quality E-commerce system as well as a methodology for evaluatingthe quality of E-commerce systems [3].

This paper proposes a model based on Bayesian Networksthat can be used for assessing the quality of E-commercesystems, as well as defming specific quality requirementsduring the design process of E-commerce systems. Section 2discusses quality issues that relate to E-commerce systemscharacteristics and that formulate the models background. Insection 3 the notation and formation of Belief Networks aredescribed, while section 4 discusses the shucture andperformance of the proposed model. Section 5 presents themodels use and applicability. Finally, in section 6conclusions and future work are discussed.

11. THE MODELS THEORETICAL BACKGROUNDMost E-commerce systems seek to provide high qualityservices to the end-users, i.e. the clients, and to this end theyinclude specific applications (modules) so as to meet specificend-user requirements. Examples of such requirements aresearching capabilities, flexible navigation or the ability togroup goods and applications like search engines, site maps

and shopping carts have been developed in order to meet suchrequirements. Even if the type of applications that an E-commerce system integrates changes in the future, the userrequirements relating to th e E-commerce system will remainunchanged. It is thus reasonable to conclude that the qualityand evaluation methods of E-commerce systems will alwaysbe dependant on the quality of similar applications and theirability to meet end-user requirements. Such quality factorsshould be taken under serious consideration during thedevelopment of E-commerce systems.Past approaches about the quality o f E-commerce systemsare emphasizing on usability standards using techniques likefeature inspection methods and collecting data about end-users opinion by questionnaires. These methods provide animportant feedback to th e researcher and their results can beutilised as a useful background for future work, however,they do not contribute directly to a dynamic model. On hecontrary, the importance of the proposed model lies on itsdynamic character. In the proposed model the results derivedfrom its application are utilized for the models constant

Corresponding author: M ichalis Xenos is IEE E M ember since 19980-7803-7785-0/03/$17.002 0 0 3 IEEE .

13


2/6

improvement, thus contributing to a continuous evolvementand upgrading.The proposed model is based on the I S 0 9126 qualitystandard [4]. Specifically, it relies on the set of those qualitycharacteristics and sub-characteristics that are directly relatedto quality as perceived by the end-users. These qualitycharacteristics are: functionality, usability, reliability andefficiency. The importance of each of the above mentionedquality characteristics depends on each E-comm erce systemsspecificities as well as the user requirements and developerpriorities for the specific system. It should be m entioned thatthe developme nt of the proposed model was mainly based onBusiness to Consumer (B2C) systems.

Ill. BELIEF NETWOR KSThe proposed model is based on the notation andformation of Causal Probabilistic Networks, also calledBelief Networks (BN) and Bayesian Networks [ 5 , 61. Themathematic model on which B ayesian Networks are based isthe theorem developed by the mathem atician and theologianThomas Bayes. The BN are a special category of graphicmodels where nodes represent variables and the directedarrows the relations between them. Therefore, a BN is agraphic network that describes the relations of probabilitiesbetween the variables 171.

Fig. 1. A simple Bayesian NetworkTh e use of BN not only makes it possible to define therelation between the various nodes (variables), but also toestimate consistently the way in which the initial probabilitiesinfluence uncertain conclusions, such a s the quality of an e-commerce system. In this case, BN are used for futureestimation, or -as also c a l l e 6 forward prediction.Furthermore, BN can be used to speculate about the states ofthe initial nodes, based on a given final and snmeintermediate variables. This is called backwardassessment.In order to defme the relations between the variables,firstly the dependent probabilities that describe the relationsbetween a child node and its parent nodes must bedetermined for each node. If the values of each variable aredistinct, then the probabilities for each node can be described

in a Node Probability Table. This table presents theprobability that a child node is assigned a certain value foreach combination of possible values of the parent nodes.For example, figure 1presents two parent nodes (nodesB an d C ) and one child node (node A). The probability tableof node A reflects the probability P(AlB,C) for all possiblecombinations of A, B, C. Thus, since there are two possiblestates for node B (bl, b2) of figure 3, three possible states fornode C (e/ , c2, c3) an d three for node A (al, 2, o3), then theNPT o f node A will include 3*2*3=18 elements.

IV. MODELS DESCRlPPlONThe philosophy underlying the proposed model is thecreation of a dynam ic network that concentrates and exploitsthe knowledge gained from the analysis of data gatheredduring previous researches and that can also use it s ownresults for future estimations. A graphical presentation of th enetwork is illustrated in Figure 2.The model uses nodes to represent the quality factors,characteristics and sub-characteristics of E-commercesystems. Each node is characterized by a set of possible statescalled evidence and is connected to i& parent nodes bydirected arrows. In figure 2the central node Quality appearsin grey. This node represents the E-comm erce system qualityas a whole and is characterized by three possible states(evidence): good, average, and poor. The parent nodesof quality are the nodes: Functionality, Usability,Reliability and Efficiency, namely the quality factors thatend-users value based on IS 0 9126. These quality factors aremarked with bold letters in the correspon ding nodes of figure

2 and can also be characterized by three possible states:good, average, and poor. Each quality factor node isconnected to the corresponding E-commerce systems qualitycharacteristics, based on I S 0 9126, which in turn areassigned three possible states as e vidence: good, average,poor. Finally, each of these quality characteristics isconnected to a number of child nodes comprising the qualitysub-characteristics of E-commerce systems. The evidence inall nodes simply answers the question posed to the userwhether a specific characteristic or sub-characteristic exists inthe E-commerce system or not. This is a way to minimizesubjectivity a t this level as much as possible.Th e model has been developed using the Microsoil 0MSBNx Authoring and Evaluation tool version 1.4.2. Anexample of the tools user interface is shown in Figure 3.Each node of the model has a Node Probability Table thatpresents the discrete conditional probability distribution. Thistable presents the relations between this node (child node)and its parent nodes. For example, the quality sub-characteristic of Learnability is represented as a child nodeconnected to two parent nodes, as indicated by the directedarrows. Each parent node represents the relevant e-commercecharacteristics, namely: Easy help functions and Correctplacement of tools. The Leamability node has three

14


3/6

possible states as evidence and the parent nodes have two node. In the Node Probability Table of the quality sub-states for evidence. The probability table for Leamability characteristic Leamability, which is presented in figure 3,has therefore 3*2*2 = 12 elements. One of the most the values of the probabilities vary between 0 to I , scaling hyimportant factors affecting the successhl application of the 0.05. The probabilities of the model are based on data takenmodel is the definition of the Node Probability Table of each from previous studies of E-commerce systems [SI.

Fig.2. Graphical presentation oflhe proposed model


4/6

Fig. 3. Example of a Belief NetworkThe user can insert data (evidence) for one o r more nodes.This evidence can activate the conditional probabilities ofother nodes and provide an estimation using bar charts. Forinstance, in figure 3evidence has been inserted for the parentnodes of Leamability. The corresponding bar chart showsthat there is 54% probability that the systems Leamabilityis goo d is 54%.Another advantage o f the model is that it can utilize theresults from its application s in orde r to improve the accuracyof future measurements. Namely, the results are used for theimprovement of the Node Probabilities Tables thuscontributing to better accuracy.

V. APPLICATION OF THE MODELThe application of the model is based on the input of

evidence for some nodes. If no evidence is inserted by theuser, the estimations provided by the model are based onpreviously collected experience of the system, as this hasbeen inserted in the Node hobability Table. New evidenceaffects the probabilities of the other nodes and the estimationfor each node is different. This is shown clearly in the barcharts.The model can be used forwards and backwards. Forwarduse can be utilized to assess the overall quality o f an E-commerce system. In this case, the end-user inserts in each

node of the model the available evidence (measures) relatedto the E-commerce system. The model can then be used toprovide an estimation about the systems quality andcharacterize it as good, average and poor also providingthe corresponding probability values. It is worth mentioningthat the model can provide estimations even if evidence hasnot been inserted in all of its nodes. Of course, more evidenceinserted into the mod el im proves the results accurac y.In a similar way it is possible to apply the model forobtaining results about only one of the quality factors orquality sub-characteristics. In this case, the estimations of themodel can be utilized by E-commerce system developers toassess the importance of the quality sub-characteristics aswell as the interaction level between parent node s and c hildnodes. For instance, if the developer wants to assess theextent to which characteristics such as search engine,shopping cart, shopping list, altemative presentation methodsand comparative presentation of the product affect thesystems accuracy, as shown in Figure 4, it is possible to d oso by inserting various evidences for each of thesecharacteristics. The different probability values obtained bythe application of the model can assist the developer toconclude which system characteristics affect its accuracymore, and based on such conclusions to decide about the typeand the number of applications to be developed.

16


5/6

Fig.4. Exampb of forwarduse oft he modelOn the other hand, backward use of the model providesassessments regarding the intermediate nodes, when the valueof the fmal state of quality is defined. For example, if it isknown that the accuracy of an E-commerce system is goodand all the other characteristics related to accuracy at theproposed model have low probability, the model would givethe result that the probability of an accurate search enginemust be at least 85%. This indicates the importance of thischaracteristic according to the users dem ands.Similarly, for a good overall system quality andgoodsystem usability the models probabilities about attractive nesswould be 80% for good, 16% for average and 4% for poor.Therefore, in the backward use, inserting evidencesconcerning the child nodes, enables the model to provideestimations about the corresponding probability values of theparent nodes.It should be stressed that the model does not provideestimations by itself. It builds on the experience of thedeveloper. This experience is based on three components: E-commerce systems applications (modules), end-usersdemands and quality characteristics. The models estimationsare based on discrete probabilities inserted into each NodeProbability Table. Thus, the insertion of new evidence maychange the models estimations.The models dynamic character is based on the creation of

the Node Probability Tables. If the probabilities are based onaccurate data that have been systematically collected, theestimation will be accurate. However, even in the case thatthe data of the Node Probability Tables are not completelyaccurate, the model can still provide results. It can leam(collect experience) and improve the results it provides. Theinitial values of the Node Probability Tables have been

derived from measures and experiments conducted by theauthors. Affer the initial application of themodel and theconsequent improvement of the values in the NodeProbability Tables, the probability values resulting from themodels application were in agreement with relevant studiesof end-users opinions that were conducted usingquestionnaires.VI . CONCLUSIONS AND FUTURE WORK

This paper presented a model applied to assess the qualityof E-commerce systems as far as the end-user is concerned.The model does not provide results by itself but is based onend-users experience and the accuracy of the evidenceinserted into it.The model can be utilized as an important tool for theprovision of estimations conceming the quality of E-commerce systems under development and can therefore aiddevelopers during the design phase. It can also be usedbackward for the assessment of already developed E-commerce systems in order to identify problematic or highquality applications (modules). The proposed model isabstract enough so as to provide a general framework fo rassessment and estimation that can be utilized even if theform of applications comprising an E-Commerce systemchanges over time.Regarding future work, the authors goal is to provide amodel with even lower level of subjectivity. This can heachieved by analysing E-commerce systems characteristicsin a way that the user cannot provide estimation but only pre-defined answers. Furthermore, the nodes that correspond tothe E-commerce system characteristics can be further

17


6/6

analysed into one or two levels. This s a way to improve themodel's accuracy even further.

REFERENCES

[41

[71

V. Zwass, "Elecrroniecommerce: s~mclures nd issues". InternationalJoumal of Electronic Comm erce, Vol. 1, pp. 3 -23, 1996.H. Chan, R. Lee and T. Dillon, " Com m er ce Fundamenlals andApplications".John Wiley & Sons, 001.1. Bunows, "Infomarion Technology srmdords in o changing world:the role o/ ihe uerr". Computer standards & Interfaces, Vol. 20, pp.323 -331 .1999 .ISO, "ln/ormnation lechnology - Evolmlion of so/Iwore - @lilyehoracrerisrics and guides fo r their use': International Standard:lSOllEC9126, 1991.F. Jensen, "An inlroduclion o B q a i o n Neworb".UCL press, 1996.S. Lauritzen and 1. Spiegelhalter, '"Loco1 eompuloliom wirhprobobililies on graphical s~ruciwei nd rheir applicaiions lo experlsystems". Readings in Uncerlain Reasoning, San Mateo, California,w.415 -448 ,1990 .N . Fenton, P. Krause and M. Neil, ' 'Sofwme MeoruremenlCincenloimly and C m d Modellmg". IEEE Soilware, pp. 116-122,JulylAugust2002.A. Stefani and M. enos, "A modcl for assessing the qunliry o/e-commerce Svslems". Proceedinas of the Panhellenic Conference withInternational Participation in Human Computer Interaction (PC-HCI2001),pp. 105- 109,2001.

18

modelling e-commerce systems’ quality with belief networks

Documents