User Profiles in Commercial Interaction Web Portals

Carmen Moraga

, Mª Ángeles Moraga

, Angélica Caro

, Coral Calero

and Rodrigo Romo Muñoz

Alarcos Research Group-Instituto de Tecnología y Sistemas de la Información, University of Castilla-La Mancha,

Paseo de la Universidad 4, Ciudad Real, Spain

Department of Computer Science and Information Technologies, University of Bio Bio, Chillán, Chile

Department of Business Management, University of Bio Bio, Chillán, Chile

Keywords: Data Quality, Web Portal, Statistical Method, Commercial Interaction.

Abstract: The use of Web portals to carry out on-line transactions is continuously increasing. This paper studies this

type of Web portals (which we have denominated as Commercial Interaction) from the perspective of data

quality, since we consider that this is an essential element if Web portals are to be competitive and their use

is to be boosted. The quality of data in a Web portal is determined on the basis of a set of data quality

characteristics. However, we have established that some data quality characteristics are more important than

others according to various user profiles, based on demographic aspects (gender, age range, level of studies

and type of organisation). In order to do that, Commercial Interaction Web portal users were surveyed and,

as a result of that, three different user profiles were identified. This paper describes the survey development

and the generation of the user profiles.

1 INTRODUCTION

The amount of activities that can be carried out via

the Internet is increasing on a daily basis, and the

Internet has come to be used in all aspects of our life

in recent years (Komathi and Maimunah, 2009). One

means to access information on the Internet is

through Web portals.

Web portals can be classified into various groups

according to the principal type of activity that users

wish to carry out. These groups are:

 ‘The Search for and Reading of Information’:

defined as those portals that the user principally uses

to obtain information (e.g., a newspaper Portal, etc.)

 ‘Commercial Interaction’: this is used to carry out

some kind of on-line transaction, such as buying

train or airline tickets.

 ‘Interaction with other People’: the important

aspect here is the ability to relate to or get in contact

with other people, known or otherwise, as is the case

of social networks.

We focus on ‘Commercial Interaction’ Web

portals, in which all types of on-line transactions are

carried out, due to they are increasingly used.

In these Web portals, the data quality (DQ), which

is often defined as the ability of a collection of data

to meet user requirements (Cappiello et al., 2004);

(Strog et al., 1997); (Wang and Strong, 1996), is

increasingly more important if user loyalty is to be

maintained and new users are to be attracted. Data

quality is a multi-dimensional concept, and is relative

to the context in which it is applied (Katerattanakul

and Siau 1999); (Shanks and Corbitt 1999). Bearing

this in mind, we have therefore studied the DQ in the

context of ‘Commercial Interaction’ Web Portals by

using the data quality model denominated as SPDQM

(SQuaRE-Aligned Portal Data Quality Model)

(Moraga et al., 2009).

In this paper, we consider that it is interesting to

establish whether different user profiles exist as

regards preferences towards the various DQ

characteristics in ‘Commercial Interaction’ Web

Portals. This is done by using an initial set of DQ

characteristics and surveying ‘Commercial Interaction’

Web portal users. Our study allows us to determine,

first, whether all the DQ characteristics are

important for users, and second, which are most

relevant in comparison to the others according to the

different user profiles established.

The remainder of this paper is organized as

follows: Section 2 shows the works related to our

study. Section 3 describes the DQ characteristics in

the Contextual category. Section 4 describes the

method used to create the survey, and how its results

were obtained. Section 5 shows an analysis of the

441

Moraga C., Moraga M., Caro A., Calero C. and Romo Muñoz R..

User Proﬁles in Commercial Interaction Web Portals.

DOI: 10.5220/0004352504410446

In Proceedings of the 9th International Conference on Web Information Systems and Technologies (WEBIST-2013), pages 441-446

ISBN: 978-989-8565-54-9

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

users according to their various demographic aspects.

In Section 6, the DQ characteristics that the various

user profiles consider to be most important for

‘Commercial Interaction’ Web portals are obtained.

Section 7 shows guidelines for designers and

developers. Finally, Section 8 presents our

conclusions and future work.

2 RELATED WORK

Our search of literature has led to the discovery of

several studies whose objective is to analyse the

differences in the use of the Internet according to

various demographic aspects. For example,

(Durndell and Haag, 2002); (Hupfer and Detlor

2006) state that the user´s preferences may be

different owing to, among other things, their

demographic aspects, such as their gender.

The level of a user’s studies also influences the

use of Web portals, as is stated by (Şahin, 2011)

who point out that there are different levels of

addiction to Internet use among students and other

professional groups.

And in general, as is stated by (Lenhart et al.,

2010), gender, education and even social status have

an influence on Internet use, principally as regards

wireless access.

All of the above has led us to believe that it

would be interesting to consider Web portal users’

demographic aspects when determining the data

quality in Web portals.

3 THE SELECTION OF DQ

CHARACTERISTICS

FOR OUR STUDY

SPDQM is a model which allows DQ to be

characterised in a Web portal on the basis of a set of 42

DQ characteristics organized in 4 categories: Intrinsic,

Contextual, Representational and Operational.

In this study we focus on the DQ characteristics

corresponding to the Contextual category,

since it is

the only category in which the importance that users

place on certain DQ characteristics with regard to

others may vary according to the type of Web portal.

he Contextual category represents the data

quality needs in the context of the task in hand. The

context is associated with the tasks that will be

carried out, and these tasks are influenced by the

types of Web portals. Of the three types of Web

portal identified in the previous section, in this paper

we have decided to begin with Web portals of the

‘Commercial Interaction’ type for this category. This

category is composed of 10 characteristics and 6

sub-characteristics. Further information on SPDQM

can be found in (Moraga et al., 2009).

In our study, we shall ask for both the

characteristics and the sub-characteristics since they

all refer to aspects of data quality whose importance

we wish to determine.

4 CARRYING OUT THE SURVEY

We decided to discover users’ opinions of the DQ

characteristics and to determine which of the 16 DQ

characteristics are most important in the context of

transactional activities by asking the users

themselves, and we therefore prepared a survey. In

order to do that, we used “the principles of survey

research” proposed in (Kitchenham and Pfleeger

2002).

 Survey design: the questionnaire in the survey

was made up of 21 questions, of which 4 were

related to demographic aspects (on Table 1), 16

corresponded to the DQ characteristics in the

Contextual category and 1 was related to the

definition of the term ‘Contextual’ itself. Table 2

shows some of these questions.

 Preparing the data collection instrument: The

questionnaire was written in simple language,

with questions that were easy to understand

without negative questions. We employed closed

questions with only one possible answer, using

the Likert scale of 1 to 10 in which totally

disagree was (0) and totally agree was (10).

 Validating the instrument: The initial

questionnaire used in the survey was validated in

a control test with 10 participants who had

experience in the use of Web portals.

 Selection of participants: The survey was

distributed to a heterogeneous group of 200

‘Commercial Interaction’ Web portal users from

Europe and Latin America.

 Administration and recovery of data from the

survey: The survey was distributed and collected by

e-mail or in printed format (manually). Of the total of

200 surveys that were distributed, 192 were returned.

Once the data had been obtained it was necessary

to determine whether the results were reliable, and

the Cronbach’s alpha was therefore calculated,

whose value must be over 0.6 to be considered

appropriate. In our case its value was 0.942, and this

value therefore indicated that the results were

reliable and that there was good internal consistency.

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Table 1: Questions Concerning Demographic Aspects.

Gender: Male/Female

Level of studies COMPLETED:

Primary Education Qualification / Secondary Education Qualification / Vocational Training/ University/ Post Graduate.

Type of organization with which you are linked (for study or work purposes). If there are various, please place

them in the order in which most time is dedicated to them, from greatest to least:

Education / Industrial / Commercial / Service Sector / Financial / Other (Please state which).

Age range: Under 25 / Between 25 and 35 / Between 35 and 45 / Between 45 and 55 / Between 55 and 65 / over 65.

Table 2: Questions in the Questionnaire for the Contextual Category.

1.- The data should be sufficiently detailed to facilitate the task at hand.

2.- The data obtained from a Web portal should be true and reliable (believable).

------------------------------------------------------------------------------------------------------

6.- The data provided by a Web portal should contain the appropriate and specific information for the use to which they will

be put.

7.- The data should adapt to user needs (e.g., they should be integrated into other applications or presented in different

formats).

8.- The data should be useful and specially oriented towards the user community that will utilize them.

------------------------------------------------------------------------------------------------------

14.- It should be possible to obtain the data by using the appropriate quantities and types of resources (by, for example,

using the smallest possible number of links to locate the data desired).

15.- The data obtained from Web portals should be exact and concise, thus helping you to find relevant results.

16.- The data in Web portals should provide the information that users are seeking.

17.- The data provided by Web portals should have a level of quality that accords with the specific use to which you wish to

put them, i.e., in the context of the specific area in which you wish to work with them.

Once the appropriateness of the sample had been

assured, it was possible to begin to obtain conclusions.

A statistical study was therefore performed with the

intention of determining the DQ characteristics that

are most important for different types of users

according to their various demographic aspects.

5 DESCRIPTION

OF THE PARTICIPANTS

AND THE SAMPLE

In this section we analyse the responses related to

the demographic aspects, which were obtained from the

useful surveys. Details of the results obtained are shown:

With regard to gender, the percentages are

equally distributed between men and women, of

which 50.5% are men and 49.5% are women.

With regard to age range, in our case the

majority of the sample was in the range between 25

and 35 (see Figure 2), and in general, all the user

ranges of under 55 years of age are representative.

In the case of the level of studies of the

participants surveyed, those participants with

University studies are widely represented.

Finally, as regards the type of organisation to

which the user is linked, the most representative are

‘Education’ and ‘Service Sector’.

6 DISCOVERING

USER PROFILES

We shall now analyse the results obtained from the

surveys. This will be done by following the three

steps, details of which are provided in the following

subsections.

6.1 Step 1: Descriptive Statistical Analysis

The first step involved carrying out a descriptive

statistical analysis in order to obtain the minimum,

maximum and mean values of each DQ

characteristic. This allowed us to determine whether

all of them are important to users of this type of Web

portal. It can be concluded that they all are important

for the analysis of the DQ in the Contextual category

for ‘Commercial Interaction’ Web portals because

the set of DQ characteristics as a whole was

evaluated with a mean score of over 7.2.

6.2 Step 2: Factorial Analysis

In the second step, the initial set of DQ

characteristics from the Contextual category was

used to create homogeneous groups of these DQ

characteristics (denominated as factors). These

factors summarise and synthesise the information

since they reduce the quantity of initial DQ

UserProfilesinCommercialInteractionWebPortals

443

characteristics. These groups are formed of those

DQ characteristics that have a considerable

correlation with others. Moreover, each group

should be independent from the others.

We decided to form three factors. Furthermore

the Cronbach’s alpha was calculated for each of the

factors obtained in order to estimate the reliability of

the results. Factor 1 obtained a Cronbach alpha value

of 0.925, Factor 2 obtained a Cronbach alpha value

of 0.881 and Factor 3 obtained a Cronbach alpha value

of 0.788. This signifies that the values obtained are

good, and that the results are therefore reliable.

Table 3 shows the DQ characteristics in each factor.

Table 3: Factorial analysis.

Factor 1 Factor 2 Factor 3

Scope Flexibility Novelty

Reliability Applicability Timeliness

Validity Value-added Relevancy

Traceability Usefulness Precision

Compliance

Specialization

Efficiency

Effectiveness

6.3 Step 3: Cluster Analysis

In the last step, the results obtained from the

factorial analysis were used to carry out a cluster

analysis in order to group the factors by resemblance

or similitude (denominated as clusters). These

clusters determined the importance placed on the DQ

characteristics in each factor. Three clusters were

also obtained in this case, each of which contained

one single factor. The results are shown in Table 4.

Table 4: Factors in each cluster.

Cluster

1 2 3

Factor 3 Factor 1 Factor 2

The clusters were then related to the

demographic aspects by using the contingency

tables. These tables allowed us to determine the set

of DQ characteristics that was most important

according to the cluster to which each variable of

each demographic aspect belonged.

Table 5 shows a summary of the contingency

tables, in which the clusters are related to the

demographic aspects.

As was mentioned previously, the contingency

tables allowed us to determine the set of DQ

characteristics for each variable of each

demographic aspect. This was done by using the

results from Table 5 and following the procedure

shown below:

Table 5: Relationship between demographic aspects and clusters.

Demographic

Aspect

Variable

Cluster (%)

1 2 3

Gender

Male 46 56

Female 54 44 43

Age range

Under 25 18

35 7

Between 25 and 35 28 20 39

Between 35 and 45 22 13 25

Between 45 and 55 27 26 18

Over 55 5 6 11

Level of

Studies

High School 18

24 11

Vocational Training 20 9 11

University 51 59 61

Postgraduate 11 8 17

Type of

Organization

Education

41 39 39

Industrial-commercial-

financial

15 4

Service Sector 31 24 39

Other 23 22 18

First: We determined which variable had the

greatest value for each demographic aspect and each

cluster. For example, Cluster 1 and the demographic

aspect ‘gender’ give us a value of ‘54’ which

corresponds with the variable ‘female’, while the

demographic aspect ‘level of studies’ gives us a

value of ‘51’ which corresponds with the variable

‘University’, which is also in Cluster 1.

Second: The value obtained was compared with

the other values in this variable for the other

clusters. In the example, the values are ‘44’ and ‘43’

for gender aspect in Clusters 2 and 3, respectively,

and the values are ‘59’ and ‘61’ for the level of

studies in Clusters 2 and 3, respectively.

Third: We chose the greatest of the values

obtained for this variable. In the example, we

selected the value ‘54’, which is in Cluster 1, for the

variable ‘female’, and the value ‘61’, which is in

Cluster 3, for the variable ‘University’, and this

variable was discounted in Cluster 1 (the values

shown in bold type in Table 5).

Fourth: For those variables which did not yet

have a selected value, we chose the highest value in

their row. For example, for the ‘Vocational

Training’ variable, whose values are ‘20’, ‘9’, and

‘11’ in Clusters 1, 2 and 3, respectively, we chose

the value ‘20’ which is in Cluster 1 (the values

shown in italics in Table 5).

This procedure was followed to obtain the

shaded values shown in Table 5. These results

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allowed us to place each variable of each

demographic aspect in one of the three clusters. The

set of demographic aspects of each cluster will allow

us to define a user profile. A summary of these

results is shown in Table 6.

6.4 Limitations of this Study

This work has been carried out in a systematic

manner. Nevertheless, we are conscious that it has

certain limitations. In this paper we have limited

ourselves to studying Web portals of the

‘Commercial Interaction’ type, and have obtained

those DQ characteristics which are most relevant

according to the users’ different demographic

aspects. However, all the users who responded to

our survey are from Europe and Latin America,

since we have surveyed users within a known

environment. The geographical zone to which the

users belong will be extended in future surveys by

using the snowball method.

In a future work we shall analyse the results for

other types of Web portals.

7 TOWARDS THE GUIDELINES

DEFINITION

Since we have defined the factors and clusters, and

have discovered the user profiles.

In this section, we

show the method used in order to create guidelines for

designers and developers so that they will know which

DQ characteristics are most important according to the

type of user to which the

‘Commercial Interaction’

Web portals

are oriented and which they intend to

develop or modify

. This is done as follows:

First: We identify the type of user towards whom

the ‘Commercial Interaction’ Web portal that is

going to be created or modified is targeted. The type

of user will be determined by the demographic

aspects of gender, age range, level of studies and

type of organisation with which they are linked (for

study or work purposes). The following examples

will allow the results to be analysed:

 Example 1: Men between 35 and 45 with

Postgraduate studies belonging to service

organisations.

 Example 2: Women under 25 with university

studies belonging to educational organisations.

Second: The profile to which each demographic

aspect belongs are obtained (see Table 6).

 From Example 1:

- Men belong to Profile 3.

- Those between 35 and 45 are in Profile 3.

- Those with Postgraduate studies are in Profile 3.

- Service organisations belong to Profile 3.

 From Example 2:

- Women belong to Profile 1.

- Those under 25 are in Profile 2.

- Those with University studies are in Profile 3.

- Educational organisations belong to Profile 1.

Third: Finally, it is necessary to consider the DQ

characteristics in each factor. The designers and

developers will put special emphasis on the DQ

characteristics in the factor that appears most often.

If none of the factors are repeated and various

factors are repeated the same amount of times, they

will consider the DQ characteristics of those factors.

 From Example 1: most attention should be paid to

the DQ characteristics in Profile 3 (see Table 6),

which indicates that these users are interested in data

that can be adapted to users’ needs (Flexibility), and

are also interested in data that are oriented towards a

destination community (Applicability), permit

advantages to be attained (Value-added) and are

useful (Usefulness).

 From Example 2: most attention should be paid to

the DQ characteristics in Profile 1 (see Table 6),

which is the predominant factor and indicates that

these users are interested in the fact that the data are

new (Novelty), are obtained in the least possible

amount of time (Timeliness), are applicable and

innovative (Relevancy) and are exact (Precision).

8 CONCLUSIONS AND FUTURE

WORK

The objective of this document is to analyse the DQ

characteristics for ‘Commercial Interaction’ Web

portals in order to verify whether some are more

important than others according to various user

profiles which are determined by the demographic

aspects of gender, age range, level of studies and

type of organisation to which the users are linked.

We carried out a survey containing questions

concerning the DQ characteristics identified and the

demographic aspects of the users of this type of Web

portals.

The results obtained from the surveys were then

analysed and they allowed us to determine three user

profiles, in addition to the most important DQ

characteristics for each of these user profiles.

We have therefore verified that some DQ

characteristics are effectively more relevant than

others when considering gender, age range, level of

studies and the type of organisation to which

the users are linked. For example, men place

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445

Table 6: Demographic aspects in each Profile.

Demographic aspects

Profile /

Cluster

Factors DQ Characteristics Gender Age Level of Studies Type of Organization

1 3 Novelty, Timeliness, Relevancy, Precision Female Between 45 and 55

Vocational

Training

Education

Other

2 1

Scope, Reliability, Validity, Traceability,

Compliance, Specialization, Efficiency,

Effectiveness

< 25 High School

Industrial-

commercial-financial

3 2

Flexibility, Applicability, Value-added,

Usefulness

Male

Between 25 and 45

> 55

University

Postgraduate

Services

importance on the fact that data are oriented towards

a destination community (Applicability), and users

between 25 and 35 years of age consider it relevant

that the data satisfy the users’ needs (Usefulness).

This paper also describes the criteria needed to

establish guidelines that will allow Web portal

designers and developers to discover which DQ

characteristics are most important for ‘Commercial

Interaction’ Web portal users according to their user

profiles.

As future work we shall analyse the other types

of Web portals, and the guidelines for the designers

and developers will be programmed into a free

software tool that will be available for use.

ACKNOWLEDGEMENTS

This research has been funded by the following project:

GEODAS-BC project (Ministerio de Economía y

Competitividad and Fondo Europeo de Desarrollo

Regional FEDER, TIN2012-37493-C03-01).

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