ELICITING CONSUMERS’ PREFERENCES IN SERVICE ... - MCDT Group

Journal of Industrial Engineering and Decision Making

Vol. 1, No. 1, May 2020, pp. 49-61

ISSN: 2683-5916, DOI: https://doi.org/10.31181/jiedm200101149m

49

ELICITING CONSUMERS’ PREFERENCES IN SERVICE SECTOR

VIA CONJOINT ANALYSIS: A CASE STUDY ON CREDIT CARD

Mina Mamaghani1, Mohammad Hasan Aghdaie2

1 Islamic Azad University North Tehran Branch, Iran, [email protected]

2 Department of Mechanical, Industrial and Aerospace Engineering Concordia University, Montreal Canada,

[email protected]

Article Info ABSTRACT

Article history:

Received April 1, 2020

Revised May 10, 2020

Accepted May 25, 2020

Triumphant designing a new product or service is of

paramount importance for the profitability, growth, and

success of any business. Besides, companies need to

wrestle with each other to achieve a higher market share by

offering customer-oriented products or services. The more

suitable a product is, the more likely it is to be sold. Hence,

designing a customer-oriented new product or service is an

integral part of all marketing plans. The purpose of this

paper is to propose a new approach combined with

Multiple Attribute Decision Making (MADM), in our

study Analytic Hierarchical Process (AHP) for feature

selection and Conjoint Analysis (CA) for market

simulation. A case study in one of the prestigious banks in

Iran is conducted to show the applicability of the approach.

Keywords:

Preference Modeling,

Multiple Attribute Decision

Making (MADM),

Conjoint Analysis (CA),

Analytic Hierarchical

Process (AHP),

Feature Selection,

Service Sector,

Banking industries,

Credit Card

Copyright © 2020 Regional Association for Security and crisis

management and European centre for operational research.

All rights reserved.

Corresponding Author:

Mohammad Hasan Aghdaie

Affiliation. Department of Mechanical, Industrial and Aerospace Engineering Concordia University

Email: [email protected]

1. Introduction

It is roughly the Internet that has changed the world in myriad ways, i.e. communication, shopping,

research, banking, education, and entertainment. Thanks to the Internet that has accelerated the quick growth

of e-commerce (Yu et al. 2011). Additionally, it becomes an indispensable shopping channel that has

mushroomed repeatedly not only in terms of the number of users but also based on its turnover (Bleoju et al.

2016).

In light of the fact that using e-commerce is convenient, time and money saver, and fast, a large number of

people do not frequently visit their bank physically (Scholnick et al. 2008). Plus this technology has altered

ways of viewing the money, especially the payment method (Pavía et al. 2012). Owing to its development,

applying credit cards for shopping transactions becomes a very common phenomenon (Panigrahi et al. 2009).

For instance, in the UK, the number of cards kept by each person was about 2.33 (Federal Reserve, 2012).

Another example is the US in which credit cards are the most widely applied tool for payment (Basnet &

Donou-Adonsou, 2016) and in 2003 for America, 18.3 billion credit card transactions were accounting for

$1.71 trillion (Committee on Payment and Settlement Systems, 2005). In Iran, there was a 0.11 debit card per

inhabitant in 2005, it reached to 4 in 2015, and in 2016 it is 4.2 per Iranian.

This situation is getting more and more complicated for banking industries when one adds a dynamic

environment, severe competition and reduction in traditional banking profit. This has led to more

concentration on consumers to achieve higher profits (Tsai, & Chen, 2010). In other words, it is very

important for marketers to fully understand how their consumers make their credit card related decisions (Qi

and Yang, 2003). Although a credit card is one of the services that banks provide, it may affect other

Journal of Industrial Engineering and Decision Making 1 (1) (2020) 49-61

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consumers’ acts. In other words, a consumer might choose a bank due to the fact that its service is desirable

for him/her. Ergo banking industries require having accurate models to satisfy their customers.

Decision making is difficult, if not the most complicated task for many people. Our ability to decide in

complicated situations can be considered as the main character that distinguishes us from animals. Considering

selection as a kind of decision making, a human being’s life is full of selection problems. Whether one makes

these decisions rationally or irrationally, he/she has to decide and he/she will be deeply affected by the

consequences. As a suitable decision will lead to a favourable outcome, people struggle to make the most

appropriate one. The question is, what should a decision-maker do when the process of decision making is

complex, combined with often conflict criteria and discreet alternatives? To address this problem, a new

branch of Operations Research (OR)/Management Science (MS) was developed. The sub-discipline was

named Multiple-Criteria Decision-Making (MCDM) or Multiple-Criteria Decision Analysis (MCDA).

MCDM/MCDA is useful, especially when the decision-making environment consists of multiple (often

conflicting) criteria. The pioneer of the field, Stanley Zionts in 1979 wrote an article titled: “MCDM - If not a

Roman numeral, then What?” and the MCDM acronym was introduced. Henceforth, many scientists

developed various MCDM methods to deal with real-life problems. In one famous perspective, two classes of

MCDM are Multi-Objective Decision Making (MODM) and Multiple Attribute Decision Making (MADM).

MADM methods repeatedly deal with decision-making environments in which there are discrete

alternatives in the presence of different and often conflict criteria. It could be impossible for a researcher in the

field of MADM that he/she did not study one of Thomas L. Saaty’s papers who developed the Analytical

Hierarchical Process (AHP) in the early 1970s. From that moment on, AHP becomes one of the dominant

MADM tools and it has been applied in many decision-making areas.

Preference research studies are one of the important tasks of market researchers. The question of how a

consumer evaluates different attributes (feature, function, and benefits) for a company is not only important

but it is also a vital one to answer. Statistical techniques and amongst them, Conjoint Analysis (CA) which is

originated in mathematical psychology (Luce and Tukey, 1964) is one of the most popular techniques. The

idea behind CA is that humankind evaluates the overall desirability of a choice based on a function and the

function is composed of different factors. Knowing this function is the key to predict buyer’s behavior.

Based on the above-mentioned paragraphs and to address them, the main contributions of this paper are:

(1) extracting important factors based on in-depth literature review and validating them by experts for credit

card analysis in banking industries (2) applying AHP as a MADM tool to select the most appropriate features

for CA process (3) using CA to market simulation and scenario analysis of new services, in our study credit

card (4) creating a new hybrid AHP-CA that can be applied in other industries for market simulation analysis

(5) an illustrative case study in one of the reputable banks in Iran demonstrates the capability and usefulness of

the model.

The rest of the paper is organized as follows. Section 2 reviews the background information. Section 3

presents the proposed methodology combining AHP and CA. Section 4 comprises a case study to show the

applicability of the proposed model and obtained results. Finally, conclusion and future researches directions

are provided in Section 5.

2. Background information

To follow the main aim of this paper, this section will review the relevant studies in three parts: credit card,

CA, and MADM.

2.1. Credit card

In today’s electronic society, e-commerce has become an essential sales channel for global business

(Panigrahi et al. 2009). Thanks to the Internet that every day countless people and companies purchase and

sell in this cyber network. Having had this channel for distributing and selling, people must have a tool to

make the process easier. Generally speaking, a credit card is one of the most important ones. It is defined as a

small plastic card that is issued by a bank and the holder can purchase goods or services on credit. Besides, it

is one of the services, but a critical one that banks provide for their customers. Direct marketers are greatly

interested to find how credit card buyers make their card-related decisions (Qi and Yang, 2003).

The literature on credit cards is focused on many topics. For example, Kara et al. (1994) applied CA to

study is to determine the important factors influencing the credit card selection behavior of college students. In

another study by Chan et al. (1999) a survey of techniques was conducted and a distributed data mining model

was suggested for credit card fraud detection tasks. Lee et al. (2002) integrated the backpropagation neural

networks with the traditional discriminant analysis approach to explore the performance of credit scoring.

Genetic algorithms and neural networks were used by Chen and Huang (2003) within the field of evolutionary

computation. They proposed a model that classified applicants into bad and good and try to reassign rejected

Eliciting consumers’ preferences in service sector via Conjoint analysis: A case study on credit card

51

ones a conditional acceptance. Huang et al. (2004) applied support vector machines and neural networks for a

credit rating analysis. In another study about fraud, Caudill et al. (2005) used a multinomial logit model with

missing information. Hsieh (2005) proposed a hybrid data mining approach, based on clustering and neural

network techniques to build an effective credit scoring model. Chen et al. (2006) designed a preventive model

via using binary support vector machines to decrease the risk of fraud. Crook et al. (2007) reviewed and

classified contemporary developments are consumer credit risk assessment. Chakravorti & To (2007) designed

a two-period model to study the interactions amongst consumers, merchants, and a card issuer. Panigrahi et al.

(2009) developed a credit card fraud detection including four components, rule-based filter, Dempster–Shafer

adder, transaction history database, and Bayesian learner. Bhattacharyya et al. (2011) took advantage of data

mining for credit card fraud detection. Baheri et al. (2011) applied fuzzy sets and CA to credit card evaluation.

Pavía et al. (2012) studied different types of credit card frauds and the role of cardholders’ effectiveness. Ooi

and Tan (2016) assessed factors influencing an emerging payment method Smartphone Credit Card (2016).

Basnet and Donou-Adonsou (2016) focused on the question of whether households with Internet access have

more favorable attitudes toward incurring more credit card balance. Grüschow et al. (2016) studied and

evaluated several payment instruments, including invoices, credit cards, PayPal payments, and prepayments,

from an online retailer’s perspective in terms of cost and credit efficiency.

2.2. Conjoint Analysis (CA)

Humans reveal their preferences and interests via their choices. Since the demand in a market is an

aggregation of these choices, many companies apply the results of preference measurement prior to the

development of a new product or service. In addition, the preference studies output can be an input for market

simulation, pricing research, and demand management. Thus it is greatly crucial to understand how changes in

characteristics of alternatives can affect their choices for many companies.

Preference studies can be classified into two classes: (1) compositional and (2) decompositional

approaches. The idea behind compositional methods is that the overall utility of a product with multi-

attributes can be estimated by summing perceived preference values, which are separately judged by the

respondent, of its levels (Fishbein 1967, Rosenberg 1956). On the other hand, decompositional methods first

measure the desirability of a set of complete products (stimulus) in order to find how much of the desirability

belongs to each attribute with its levels. Second, the sum of the obtained values of attributes can be considered

as the desirability of a possible product with these attributes and levels. In decompositional techniques, an

indirect way or back-door approach is applied to elicit people’s responses (Aghdaie et al. 2014).

CA is one of the most common approaches among decompositional which traces back in 1920, but the

starting point could be 1964 when two mathematical psychologists sought to solve sophisticated problems

(Luce and Tukey, 1964). The popularity of CA can be measured not only via 14,600,000 hits on

www.google.com but also by a large number of companies that use it.

The traditional full-profile approach has been a mainstay of CA for many years due to its simplicity and

effectiveness. It can be applied in pencil and paper studies (Orme, 2005). Moreover, it is useful for measuring

up to six attributes (Green and Srinivasan, 1978). The method was applied in different industries. For instance,

Myung (2003) illustrated an application of CA in Korean typography guidelines for Web pages. For another

example, Kazemzadeh et al. (2009) applied CA in office chair design. Altun and Gök (2010) used CA to find

the teachers’ service wants. Jianrong et al. (2011) studied passenger service quality by bus via CA. Another

example published by Pentus et al. (2014) is combining psychophysiological measurements in package design.

In banking studies, Dauda and Lee (2015) used CA for consumers’ preferences on future banking industries.

Lee (2016) used CA for discount room rate calculations in hotel industries. Rhee et al. (2016) applied CA to

explore how travellers select a restaurant for dining out. These papers represent only a few of many

applications of CA.

2.3. Multiple Attribute Decision Making (MADM)

OR/MS discipline can be defined as not only but mostly advanced mathematical tools that aid decision-

makers to understand, model, simulate and solve real-life dilemmas. They need these analytical techniques to

make superior decisions. What is more is that complex decision-making problems frequently consist of a great

number of elements, such as criteria and alternatives (Tafreshi et al. 2016). Having conflict criteria and

requiring satisfying more than one goal simultaneously, urged scientists to suggest another branch of OR/MS,

namely MCDM/MCDA.

MADM is one of the classes of MCDM/MCDA and the other one is MODM. They invariably deal with

problems with discrete alternatives in the presence of dissimilar and often conflict criteria.

As a result, MADM is very suitable for the evaluation of alternatives or options and opt for the most

desirable ones. On top of that, MADM is very good for participating in different parties, groups, stakeholders,

and experts under a joint problem-solving activity. In addition, MADM methods are capable of producing

http://www.google.com/


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tangible outcomes. That is why several MADM methods have been developed and applied in many decision-

making problems in a variety of areas, such as business, economy, marketing, health care, manufacturing,

energy, politics, and so on. These are only a few of an ample list of applications of MADM.

3. Methodology

This section presents a novel hybrid model, including AHP and CA for a customer-oriented credit card

design. The first part of the methodology section describes AHP as a group decision-making tool for the

feature selection process. The second part describes CA to credit card design and market simulation. Finally,

the last part explains the proposed framework for the whole process.

3.1. Analytical Hierarchical Process (AHP)

The term AHP was coined by Professor Thomas L. Saaty in 1971 when he was conducting research

projects in the US Arms Control and Disarmament Agency (ACDA) and he aimed to find an easy to

understand and implement an approach that can deal with complex decisions. Henceforth it has been applied in

a wide range of practical decision-making areas, including terrorism, transportation, planning, energy policy,

crime, healthcare, and many other areas.

AHP is based on three fundamental aspects. First, it focuses on the decomposition of the decision problem.

In other words, AHP views an ill-structured and complex problem as a hierarchical structure composed of

goals, objectives, attributes, criteria, sub-criteria, elements relevant to the decision. Then, it groups them as a

set of a hierarchical structure with levels.

Second, it concentrates on pairwise comparisons. They are comparative judgments between pairs of

attributes at the same hierarchy (level). For comparative judgments, one needs a scale. Thus Saaty defined

fundamental scale for qualitative comparisons due to human limitations for comparing very small and big

items. He defined his nine-point scale in the AHP procedure (see Table 1) to transform qualitative evaluations

into quantitative ones. Therefore, in his proposed approach not only one can say criterion X is preferred to

criterion Y but also he/she can say how much criterion X is preferred to criterion Y by dividing utility of X to

Y. The whole idea is based on ratio scaling which is ideal for measuring methods that deal with abstractions

evaluations. Ratio scaling is easy to work and allows mathematical manipulation and composition of measured

attributes when one wants to create an overall priority. One ratio scale is calculated for each criterion and it

can be meaningfully multiplied or divided. Additionally, by synthesizing priorities within the hierarchy one

can have a local priority. Using pairwise comparison to calculate the priority of one criterion to another

criterion in other hierarchy, provides a weighting procedure. Following this procedure and going through all

the elements of the hierarchy and using a series of comparisons, the overall ranking can be calculated. The

utility of an alternative is calculated via summing all weights of the attribute values, where weights are the

relative importance of attributes. Moreover, the ranking is in the form of a global priority which is based on

pairwise comparisons and the identified structure of the decision hierarchy.

Table 1. The Fundamental Scale of Absolute Numbers (Saaty, 1977)

Definition Intensity of importance

Equally important 1 Moderately more important 3 Strongly more important 5 Very strongly more important

7

Extremely more important 9 Intermediate values 2,4,6,8 For reciprocals If activity i has one of the above numbers assigned to it when compared

with activity j, then j has the reciprocal value when compared with i

Third, AHP has a procedure to find out how much each respondent’s comparisons are consistent. The

ratios (pairwise comparisons) which are frequently identified by experts or a group of decision-makers are

tested for logical consistency and validity. The Random Index (R.I.) is based on the average of Consistency

Index (C.I.) of five hundred randomly generated matrices (Ishizaka & Labib, 2011). The three AHP steps are

explained as follows:

First, decompose the complex problem into a hierarchy structure consisted of goal(s), criteria, sub-criteria

(if it is needed), and alternatives. The goal(s) should be located at the top, criteria in the middle, and

alternatives at the bottom of this hierarchy.


53

Second, the series of comparisons in levels based on Saaty’s standard scale should be done. The experts,

stakeholders, or decision-makers are responsible to conduct pairwise comparisons. The comparisons ought to

be done for all criteria at the same level and alternatives. For example, criterion number one and criterion

number two should be compared pairwisely and this procedure will follow to reach the end. If the problem

consists of m criteria and n alternatives, ( 1) / 2m m

comparisons are needed for the criterion matrix.

Besides, all alternatives should be evaluated based on the same scale concerning each criterion. To solve the

problem, m the numbers of (n× n)

the matrix should be created as well. The result of the pairwise

comparisons on m criteria can be summarized in an (n× n)

evaluation matrix A in which every

element ij i, j = 1,2,...,na

is the quotient of weights of the criteria, as shown in Equation (1):

11 12 1

21 22 2

1 2

...

...,

...

n

n

n n nn

a a a

a a aA

a a a

1, 1 , 0ii ji ij ija a a a

(1)

Third, to find the relative weights for each matrix, the mathematical process commences normalizing. The

relative weights are given by the right eigenvector (w) corresponding to the largest eigenvalue max( ), as

maxA w w

(2)

The matrix A has rank 1 and max n , if the pairwise comparisons are completely consistent. The

weights can be obtained by normalizing the rows or columns. The logical consistency can be tested by the

relation between the entries of : ij jk ikA a a a

via C.I. .

maxC.I. = 1n n

.

(3)

The final Consistency Ratio(C.R.)

is based on dividing C.I.by R.I. ; the aim of using C.R. is to conclude

whether the evaluations are sufficiently consistent or not. C.R. is calculated as the ratio of as indicated in

Equation (4).

C.R. = C.I. R.I.

(4)

Table 2 shows the average consistency values of these matrices. The maximum acceptable C.R. index is

0.10. If the final C.R. exceeds this value, the evaluation procedure has to be repeated until reaching the

acceptable ratio. The C.R. index ought to be used to calculate the consistency of all the hierarchy and all

matrix evaluations.

Table 2. Random Index (Saaty, 1977; Saaty, 2013)

Number of n

elements

1,

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Average

Random

Index (R.I.)

0 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 1.52 1.54 1.56 1.58 1.59


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3.2. Conjoint Analysis (CA)

CA is a multivariate technique which is useful for understanding customer preferences for product or services. The product or service is composed of attributes and their levels. In the ordinal approach, linear models are applied based on the assumption that the value or utility of the product or service is the linear summation of values or utilities of attribute levels. The total part-worth of a product or service with m attributes with the maximum number of n levels for each attribute can be obtained via the following formula:

1 1T=

m n

ijj i

ijx w

(5)

Where, T is the total part-worth of the product, ijx is equal to one, if jth performance attribute is set

to level i and is equal to zero otherwise. ijw is the part-worth of the j attribute at the i performance

attribute level.

4. The framework

In this section, the proposed framework is explained. The framework which is depicted in Figure 1 has five phases. In the first phase, the utmost important criteria for credit card design based on an in-depth literature review were gathered. Then, a group of experts evaluated the criteria and the final list was constructed. In the second phase, the AHP method was used as a group decision-making tool by experts to rank criteria. Next, the validity of evaluations was checked with the C.R. index. In the third phase, CA applied the selected criteria to product profile design. Then, the sampling was done. Finally, in the interpretation phase, obtained results were applied to provide useful information for deciding the best credit card.

Figure 1. The proposed procedure

5. Case study

To demonstrate the applicability of the model, we conducted a case study in one of the very famous and reputable banks in Iran. XYZ Bank is the fourth major Iranian private bank, headquartered in Tehran, Iran. Based on the CEO’s comments, in 2018, 23.7 billion shares have been exchanged on the Tehran Stock Exchange and counts over 70,000 shareholders, and 6.5 million customers of the bank used transactions for the majority of imports of foodstuffs, medicine, and other humanitarian trade items.

6. Results and discussion

https://en.wikipedia.org/wiki/Banking_and_insurance_in_Iran


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This section concentrates on obtained numerical results. In this study, we use expert’s evaluations, and sampling data to illustrate the proposed framework empirically. The next section explains about choosing the most important criteria by using pairwise comparisons. The conjoint analysis procedure is provided in the last section.

6.1. Criteria Selection and Pairwise Comparison

In this phase, a group of experts participated to run the project. So, the literature review was conducted to gather the most suitable criteria for credit card design. Table 3 shows the selected criteria list and their definitions.

Table 3. The definitions of credit cards factors

Variables Definitions

Balance Transfer Fee (C1) The money that one is charged when they transfer credit card debt from one card to another.

Annual Fees (C2) The fee that is charged for using the credit card service. Over-the-Limit Fee (C3) It is the money that you pay when your balance goes over your credit limit Interest Rate (C4) The price that a customer pays for borrowing money. Late Payment Fee (C5) The money that is charged to a borrower who misses paying at least their

minimum payment by the payment deadline. Credit Limit (C6) The maximum amount of credit that is extended to a customer. Brand (C8) The brand of the credit card. Foreign Transaction Fee (C7) It is money that a customer is charged for each transaction made abroad. Brand (C8) The brand of the credit card.

After creating the list of criteria, the group of experts was asked to do the pairwise comparisons in order to select the most relevant features. They used the scale provided in Table 1 to do the comparisons. All the pairwise comparisons and the calculated weights of the criteria are showed in Tables 4. We use Equations 1 to 4 for AHP calculations. The last column of Table 4 shows the weight of each criterion. Using the last column, one can see the rank/priority of each criterion. Based on Table 4, interest rate has the highest rank. The calculated C.R. factor for Table 4 is 0.07 and it is in the acceptable range (see Table 2). The top six ranked criteria are selected for CA in the next step.

Table 4. Pairwise comparisons between any two attributes

C1 C2 C3 C4 C5 C6 C7 C8 Weights C1 1.00 0.25 0.33 0.17 0.33 0.50 1.00 0.50 4% C2 4.00 1.00 2.00 0.50 2.00 2.00 4.00 2.00 17% C3 3.00 0.50 1.00 0.13 0.13 2.00 3.00 2.00 9% C4 6.00 2.00 8.00 1.00 2.00 3.00 6.00 4.00 30% C5 3.00 0.50 8.00 0.50 1.00 2.00 3.00 2.00 17% C6 3.00 0.50 0.50 0.33 0.50 1.00 9.00 1.00 11% C7 1.00 0.25 0.33 0.17 0.33 0.11 1.00 0.50 4% C8 2.00 0.50 0.50 0.33 0.50 1.00 2.00 1.00 8%

6.2. Conjoint Analysis

CA is one of the most suitable tools for understanding customers’ preferences. It is a widely accepted market research tool that is capable of designing and pricing a new product or service. The following steps are essential to perform for CA (Wedel and Kamakura, 2012; Gustafsson et al. 2003):

1. Identifying the attributes and levels: The first step of CA is to choose the attributes and their levels. The features/attributes are the key components that a customer uses them to evaluate a product or service. We used the AHP method for the selection of the attributes in order to reduce the number of features in CA. Table 5 shows six attributes and their levels. The main attributes are: Interest Rate, Annual Fees, Late Payment Fee, Credit Limit, Over-the-Limit Fee, and Brand.

Table 5. Conjoint attributes and attribute levels


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Attributes Levels, descriptions (Number of level in 29 profiles) A1 Interest Rate (%) L11: 35% (11/29) L12: 40% (6/29) L13: 55% (6/29) L14: 65% (6/29) A2 Annual Fees (Rial) L21: 1,500,000 (12/29) L22: 2,500,000 (5/29) L23: 4,500,000 (6/29) L24: 6,000,000 (6/29) A3 Late Payment Fee (%) L31: 0.01% (11/29) L32: 0.02% (6/29) L33: 0.03% (7/29) L34: 0.04% (5/29) A4 Credit Limit (Rial) L41: 100,000,000 Year (11/29) L42: 200,000,000 (10/29) L43: 500,000,00 (8/29) A5 Over-the-Limit Fee (Rial) L51: 1,000,000 (13/29) L52: 1,500,000 (5/29) L53: 2,000,000 (6/29) L54: 3,000,000 (5/29) A6 Brand L61: B1 (12/29) L62: B2 (6/29) L63: B3 (6/29) L64: B4 (5/29)

2. Stimulus set construction: In this study, we used a full-profile for CA. The full-profile has been a mainstay approach for decades in CA (Orme, 2005) and it can be applied to measure up to six attributes (Green and Srinivasan, 1978). To create a set of profiles for CA, we need to make a list of potential products based on different possible combinations of selected attributes. More precisely, the total number of hypothetical profiles of credit cards is calculated by multiplying the number of levels associated with each attribute. In our case study, it is 4 × 4 × 4 × 4 ×4 × 3 = 3,072 hypothetical profiles, which is a big number.

From the customers’ point of view, it is impossible to evaluate all the hypothetical profiles; and it is necessary to select a subset of them in a way that the performance of the evaluations stays the same. To solve this problem, fractional factorial design frequently is applied by many researchers and fractionates (Naes et al. 2001). To generate the profiles, we use SPSS-18.0. 29 profiles were generated by the software. Out of 29 profiles, 4 of them are called holdout profiles. Holdout profiles are designed to assess the reliability and validity of the response. An example of a profile card is shown in Table 6. Table 7 depicts a few numbers of profiles. A ten-point scale is used for evaluations. 3. Stimulus presentation: For collecting data we used a questionnaire.

4. Part-worth utility calculation

5. Calculating the relative importance of each attribute

6. Evaluating and interpreting the results

Table 6. Example of a profile card

Profile number: 9 How likely are you to purchase this Credit Card? .........

Interest Rate 40% Annual Fees 2,500,000 Late Payment Fee 0.03% Credit Limit 200,000,000 Over-the-Limit Fee 1,500,000 Brand B1 Least preferred …………………………………….......... Most preferred


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1 2 3 4 5 6 7 8 9 10 □ □ □ □ □ □ □ □ □ □

Table 7. Design of profiles

Profiles A1 A2 A3 A4 A5 A6

L11 L12 L13 L14

L21 L22 L23 L24

L31 L32 L33 L34

L41 L42 L43

L51 L52 L53 L54

L61 L62 L63 L64

1

… 10

… 20

… 29

The data was gathered using a questionnaire survey. The total sample size consists of 600 respondents, 260 females, and 340 males. All respondents were Iranian, aged 18 years old or older. The questionnaires were distributed in the Bank’s branches. The branches and the customers are selected randomly. Table 8 shows a wide range of socio-demographic information such as age, gender, marital status, education, occupation, and income.

Table 8. Socio-demographic characteristics of the sample (% of respondents, n = 600)

Gender Income class ($) Male 56.7 <= 400 71.3% Female 43.3 400-800 15.1% 800-1200 7.1% 1200-1600 3.6% >=1600 2.9% Age Education 18 to 24 40.3 Diploma or under 6.1% 24 to 30 33.4 Bachelor degree 58.4% 30 to 40 14.5 Master degree 26.7% > 40 11.8 PhD 8.8%

Table 9 shows the relative importance of each attribute and its levels. The last column of the table shows the average utility scores. For example, respondents preferred a high credit limit with a low interest rate. The negative sign shows that the attribute’s preference declines when the value increases. Based on the results, interest rate has the highest utility.

Table 9. The part-worth utility and relative importance for all the customers

Levels The part-worth utility The relative importance Overall Overall A1 L11 -.662 26.604 L12 -1.324 L13 -1.986 L14 -2.648 A2 L21 -.652 18.205 L22 -1.008 L23 -1.066 L24 -.828 A3 L31 -.415 16.357 L32 -.830 L33 -1.244 L34 -1.659 A4 L41 .622 16.098 L42 1.245


58

L43 1.867 A5 L51 -.101 9.179 L52 -.202 L53 -.304 L54 -.405 A6 L61 .334 13.557 L62 .668 L63 1.003 L64 1.337

To check the reliability, the goodness of fit for the estimated conjoint model is calculated using two measures. we found out that the value of Kendall’s tau is 0.903 and the value of Pearson’s R is 0.976 for all the samples. We have also used four stimuli as validation or holdout stimuli to determine internal validity. Parameters from the estimated conjoint model (using 25 stimuli) were used to predict preferences for the holdout set of stimuli and then they were compared with actual responses by calculating correlation. Considering the table (Table 10), we have found out that the value of Kendall’s tau is 1.000 for the four holdout cases in the overall sample and two segments. So, we can say that our conjoint model has high predictive accuracy and, internal validity.

Table 10. Correlations

Measure Overall

1 Pearson's R .976

2 Kendall's tau .903

3 Kendall's tau for Holdouts 1.000

7. CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH

In the preference modelling literature, the question of how human beings evaluates and chooses an alternative, for example buying a product is of paramount importance. Since one of the goals of marketing is providing a customer-oriented product or service, market researchers always seek to find how consumers evaluate and choose a product or service out of myriad goods in the marketplace. Answering this significant question can pave the way for other company’s strategies and plans. Moreover, the market is bursting with demanding customers, a multitude of competitors, and the overly dynamic environment. Similar to many companies, banks are invariably seeking for finding novel ways to elicit their consumer’s minds to absorb or keep them. Considering the above-mentioned conditions, banks need to provide the best possible services for their customers via eliciting their wants and needs.

The aim of this paper is to use AHP as a MADM tool and CA as a preference modelling tool to elicit preferences of credit card users. More precisely, AHP was applied to rank the selected criteria from the most to the least important ones. Then, the CA process used them to find the optimum combination of the factors for designing the most suitable credit card based on customer evaluations.

The paper has some limitations and drawbacks. Firstly, the sample size and sampling process are based on the available time and budget of the researchers. Secondly, the case study is conducted in one of the banks in Iran and the results and discussion are based on that. Thus the scale of this study is somehow small.

Future works will be based on addressing these themes. One possible theme is using fuzzy logic in conjoint and AHP methods and compares the results. Furthermore, this study showed that the selected factors can significantly influence the part-worth utilities so it is highly recommended to add or delete some of the evaluation criteria to see whether the ranking may change or not. In this paper, AHP was applied to select the factors, however, using other MADM methods can be considered as a new study. Moreover, applying other CA methods, such as Adaptive Conjoint Analysis (ACA) and choice-based conjoint analysis (CBA) or MaxDiff analysis is also recommended. Additionally, it is not possible to design one product for the whole market. Thus it would be a good idea to combine conjoint analysis with market segmentation (See Aghdaie et al. 2014). Although the model was applied in the service industry, in our study banking industries, it is possible to use the model in other industries, or in other countries.


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ACKNOWLEDGMENTS: The authors express their gratitude to the respectful editors for their constructive, valuable and encouraging comments.

REFERENCES

Aghdaie, M. H., Tafreshi, P. F., & Behzadian, M. (2014). Customer-oriented benefit segmentation: an

integrated approach, International Journal of Business Innovation and Research, 8(2), 168-189.

Altun, A., & Gök, B. (2010). Determining in-service training programs’ characteristics given to teachers by

conjoint analysis. Procedia-Social and Behavioral Sciences, 2(2), 1709-1714. Applications, Springer, Berlin.

Baheri, E., Dalvand, M. R., Ansarinejad, A., Miri-Nargesi, S., & Hatami-Shirkouhi, L. (2011). A fuzzy

conjoint analysis approach for evaluating credit card services: A case study of Iranian bank, African Journal of

Business Management, 5(7), 2753-2765.

Basnet, H. C., & Donou-Adonsou, F. (2016). Internet, consumer spending, and credit card balance: Evidence

from US consumers, Review of Financial Economics, 30, 11-22.

Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A

comparative study, Decision Support Systems, 50(3), 602-613.

Bleoju, G., Capatina, A., Rancati, E., & Lesca, N. (2016). Exploring organizational propensity toward

inbound–outbound marketing techniques adoption: The case of pure players and click and mortar companies,

Journal of Business Research, 69(11), 5524-5528.

Caudill, S. B., Ayuso, M., & Guillén, M. (2005). Fraud detection using a multinomial logit model with missing

information, Journal of Risk and Insurance, 72(4), 539-550.

Chakravorti, S., & To, T. (2007). A theory of credit cards, International Journal of industrial organization,

25(3), 583-595.

Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (1999). Distributed data mining in credit card fraud

detection, IEEE Intelligent Systems and Their Applications, 14(6), 67-74.

Chen, M. C., & Huang, S. H. (2003). Credit scoring and rejected instances reassigning through evolutionary

computation techniques, Expert Systems with Applications, 24(4), 433-441.

Chen, R. C., Chen, T. S., & Lin, C. C. (2006). A new binary support vector system for increasing detection

rate of credit card fraud, International Journal of Pattern Recognition and Artificial Intelligence, 20(2), 227-

239.

Committee on Payment and Settlement Systems, 2005. Statistics on Payment and Settlement Systems in

Selected Countries. Bank for International Settlements, Basel, Switzerland.

Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk

assessment, European Journal of Operational Research, 183(3), 1447-1465.

Federal Reserve (2012). Federal Reserve’s G19 report on consumer credit. March, 2012.

Fishbein, M. (1967). A Behavior Theory Approach to the Relations between Beliefs about an Object and the

Attitude towards the Object, in Readings in Attitude and Theory Measurement. New York: John Wiley &

Sons, 389-99. Kluwer Academic Publishers, New York.

Green, P.E. and Srinivasan, V. (1978) ‘Conjoint analysis in marketing: new development with implications for

research and practice’, Journal of Marketing, 54(4), 3–19.

Grüschow, R. M., Kemper, J., & Brettel, M. (2016). How do different payment methods deliver cost and

credit efficiency in electronic commerce?. Electronic Commerce Research and Applications, 16, 27-36.

Gustafsson, A. Herrmann, A. and Huber, F. (2003). Conjoint Measurement: Methods and Research, Research

publishers LLC, New York.

Hsieh, N. C. (2005). Hybrid mining approach in the design of credit scoring models, Expert Systems with

Applications, 28(4), 655-665.

Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector


60

machines and neural networks: a market comparative study, Decision support systems, 37(4), 543-558.

Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process, Expert

systems with applications, 38(11), 14336-14345.

Jianrong, L. I. U., Wei, D. E. N. G., & Zhang, B. (2011). Conjoint analysis based transit service quality

research, Journal of Transportation Systems Engineering and Information Technology, 11(4), 97-102.

Kara, A, Kaynak E, Kucukemiroglu O (1994). “Credit Card Development Strategies for the Youth Market.”

International Journal of Bank Marketing, 12(6): 30–36.

Kazemzadeh, R. B., Behzadian, M., Aghdasi, M., & Albadvi, A. (2009). Integration of marketing research

techniques into house of quality and product family design, The International Journal of Advanced

Manufacturing Technology, 41, 1019.

Lee, S. H. (2016). How hotel managers decide to discount room rates: A conjoint analysis, International

Journal of Hospitality Management, 52, 68-77.

Lee, T. S., Chiu, C. C., Lu, C. J., & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant

technique, Expert Systems with applications, 23(3), 245-254.

Luce, R. D. and Tukey, J. W. (1964). Simultaneous conjoint measurement-a new type of fundamental

measurement, Journal of Mathematical Psychology, 1(1), 1–27.

Malhotra, R., & Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-

fuzzy systems, European Journal of Operational Research, 136(1), 190-211.

Myung, R. (2003). Conjoint analysis as a new methodology for Korean typography guideline in Web

environment, International Journal of Industrial Ergonomics, 32(5), 341-348.

Naes, T., Kubberod, E. and Sivertsen, H. (2001). Identifying and interpreting market segments using conjoint

analysis, Food Quality and Preference, 12(2), 133–143.

Ong, C. S., Huang, J. J., & Tzeng, G. H. (2005). Building credit scoring models using genetic programming,

Expert Systems With Applications, 29(1), 41-47.

Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile

users to explore smartphone credit card, Expert Systems With Applications, 59, 33-46.

Orme, B. (2010). Getting started with conjoint analysis: strategies for product design and pricing research

second edition, Madison: Research Publishers LLC.

Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2009). Credit card fraud detection: A fusion approach

using Dempster–Shafer theory and Bayesian learning, Information Fusion, 10(4), 354-363.

Pavía, J. M., Veres-Ferrer, E. J., & Foix-Escura, G. (2012). Credit card incidents and control systems,

International Journal of Information Management, 32(6), 501-503.

Pentus, K., Mehine, T., & Kuusik, A. (2014). Considering Emotions in Product Package Design through

Combining Conjoint Analysis with Psycho Physiological Measurements, Procedia-Social and Behavioral

Sciences, 148, 280-290.

Qi, M., & Yang, S. (2003). Forecasting consumer credit card adoption: what can we learn about the utility

function?, International Journal of Forecasting, 19(1), 71-85.

Rhee, H. T., Yang, S. B., & Kim, K. (2016). Exploring the comparative salience of restaurant attributes: A

conjoint analysis approach, International Journal of Information Management, 36(6), 1360-1370.

Rosenberg, M. J. (1956). Cognitive Structure and Attitudinal Affect, Journal of Abnormal and Social

Psychology, 53, 367-72.

Saaty, T. L. (2013). The modern science of multicriteria decision making and its practical applications: the

AHP/ANP approach, Operations Research, 61(5), 1101-1118.

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures, Journal of Mathematical

Psychology, 15, 59–62.


61

Srivastava, A., Kundu, A., Sural, S., & Majumdar, A. (2008). Credit card fraud detection using hidden Markov

model, IEEE Transactions on Dependable and Secure Computing, 5(1), 37-48.

Tafreshi, P. F., Aghdaie, M. H., Behzadian, M., Abadi, M., G. (2016). Developing a Group Decision Support

System for Advertising Media Evaluation: A Case in the Middle East, Group Decision and Negotiation, 25(5),

1021–1048.

Tsai, C. F., & Chen, M. L. (2010). Credit rating by hybrid machine learning techniques. Applied Soft

Computing, 10(2), 374-380.

Wedel, M. & Kamakura, W. A. (2012). Market Segmentation Conceptual and Methodological, Vol. 8. ,

Springer Science and Business Media.

West, D. (2000). Neural network credit scoring models, Computers & Operations Research, 27(11), 1131-

1152.

Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a

strategy for credit card fraud detection, Data Mining and Knowledge Discovery, 18(1), 30-55.

Yu, X., Guo, S., Guo, J., & Huang, X. (2011). Rank B2C e-commerce websites in e-alliance based on AHP

and fuzzy TOPSIS, Expert Systems with Applications, 38(4), 3550-3557.

Zionts, S. (1979). MCDM-If not a Roman Numeral, then what?. Interfaces, 9(4), 94-101.

© 2020 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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