Suggested Model to measure the Value of Syrian Mobile ... · 4. Conjoint analysis Third step a market simulator have been done, which is usually the most important tools in conjoint

49

International Journal of Scientific Research and Innovative Technology Vol. 6 No. 1; January 2019

Suggested Model to measure the Value of Syrian Mobile Phone

Operator's Brand Names using Conjoint Analysis

1Dr. Ahmad Taha Kahwaji, Ph D in Strategic Management (Brescia University – Italy),

Assistant Professor in College of Commerce and Business Administration at Dhofar

University, Sultanate of Oman, e-mail: [email protected]

2Mr. Wissam Abou Khalel, Damascus University, Higher Institute for Administrative

Development, Damascus Syria and Scientific Researcher in Tubingen University, Germany,

e-mail: [email protected]

Abstract: Brand is a very important intangible asset for organization. In this paper nominal values

of Syrian mobile phone operators' brands were measured by using Conjoint Analysis technique

depending on the preference of Syrians people which introduce good data measurements for mobile

operators in Syria in order to prepare good Brand strategic plans.

Keywords: Brand; Conjoint Analysis; Value; Syrian Mobile Phone Operators

1. Introduction

Many companies' managers think that Brand management is the job of marketing division group.

This is not true in today's world companies, because it is the job of the top managers to reach the

organization or the company to the expectations of all stakeholders (internal &external), and brand

management is the key of the long term success (P.Kotler, 2006). The Brand considered being the

most important intangible assets that organization own is the main resource for gaining competitive

advantages(Neal & Strauss, 2008), and to manage the Brand well the top managers should use a

special skills and tools to measure the Brands names of their companies in order to design and

perform a successful marketing strategies to their Brands . This is the case in all Brands in the world

including Mobile phone operator's Brands.

At the end of 2018, 3.9 billion people uses the Internet, which represents 51.2% of individuals, one

significant step toward global information system. In developed countries, four out of five people

are online, reaching saturation levels. In developing countries, though, there is still ample of room

for growth, with 45 per cent of individuals using the Internet especially for communications. While

fixed-telephone subscriptions continue their long-term decline, mobile-cellular telephone

subscriptions continue to grow. Although the number of mobile-cellular telephone subscriptions is

already greater than the global population (ITU, 2018).The telecommunication sector plays an

important role in the global economy, with global retail telecommunication revenues reaching USD

1.7 trillion in 2016, representing 2.3 per cent of global GDP. In Syria telecommunication sector,

which recorded a fictional profitability according to the report submitted to the Syrian Financial

50

ISSN: 2313-3759 www.ijsrit.com

Markets Authority, the results of its work at the end of 2017.Syriatel,for example, recorded a profit

of USD 78.4 Million and its share price increased to USD 336.7 Million (LBD 91.8 $ million)the

highest share in the history of telecommunications since its established. MTN was significantly

lower than its counterpart, recording USD 12.6 Million.

Many techniques were used to measure the nominal value of brand name mathematically. Conjoint

analysis is one of many techniques that has been widely used to evaluate consumer preference in

marketing researches for hypothetical product and services (Hair & Black, 1995) (Kuzmanovic &

Martic, an approach to competitive Product line Design using conjoint analysis, 2012)(Kuzmanovic

& Martic, 2012), as well as for pricing research (Obradovic & Kuzmanovic, 2010),also in retail

business (jeanselme.M & Reyolds.J., 2006), the method has applied to understand the preferences

in various other fields of marketing like educations (Sohn & Ju, 2010), transportation (Hensher,

2001), telecommunications(Kim, 2004) (Sobolewski & Czajkowski, 2012), health care and hospital

services (Kuzmanovic, Vujosevic, & Martic, 2012)(Nicola & farraj),and the labor market in the

context of personnel selection decisions (Popovic M., 2012). However, few studies have used

conjoint analysis in the field of mobile industry (Head, 2010) (kim, 2008) (Nakamura, 2010). In

Syria there is no CA research paper so this paper considers the first try in CA tools in Syria.

2. The aim of the article

The general aim of this study was to estimate and assess the relative importance of the relevant

service attributes (price, brand, coverage), and the presence of brand name in the decision of

purchase postpaid mobile phone in Damascus city (capital of Syria) in order to measure the value

of the brand names utility. The specific aims were: to determine differences in the consumer

preferences regarding postpaid mobile phone according to its company, and to measure the utilities

of both brand names (Syriatel, MTN), The results of this research are expected to (1)accept or

reject the Null Hypothesis which says: There is no statistics significant relation between Brand

names and Syrian respondents and consequently on buy decision for Syrian consumers of mobile

phone postpaid lines.(2) apprise mobile phone operators of Syrian expectations in terms of new

aspects of services provided, and create new design business models, to formulate marketing

strategy based on Syrian respondents’ needs.

3. Material and methods

3. 1. Attributes and their levels

In order to determine attribute combinations, Churchill and Lucobucci (Churchill & Lacobucci,

2002) propound that the researchers make the range for the various attributes somewhat larger than

the range normally found but not so large as to make the options unbelievable, researchers cannot

expect a respondent to provide meaningful judgments if there are four attributes and three levels

(3×3×3×3=81) each of rank order judgment. Basically, three approaches can be used, namely:

verbal description, paragraph description, and pictorial representation (Ernest & Retha, 2002) .we

used a special approach, a personal interview was conducted with sampling of 50 consumer (live in

Damascus city), who are responsible of purchasing mobile phone and paying the cost of phone calls

in advance (postpaid) to them and to their family members and live in the safe area in Damascus

city (away from the civil war in Syria). The personal interview was conducted in one public

organization with 500 employees live in all residential area in Damascus. One question with

51


multiple answers was conducted between March and April 2018. This question with closed

multiple answers was used to know the most tangible and intangible attributes wanted by Syrian

consumers in Syrian mobile companies to be changed for better. These chosen attributes were

coverage area (call quality), technical support, price, social responsibility and internet service. The

favorite attributes were (coverage and price) with 35% and 50% respectively.

Figure 1: The tested tangible & intagible attribute in peresonal interview

3. 2. Questionnaire:

According to the result of personal interview, questionnaires was conducted through internet social

media and E-mails of acquaintance and all people from Damascus city, and had a prepaid mobile

phone, this questionnaire was established on free domain hosting after validation through pre-test

with 10% of the sampling from Damascus city.

The questionnaire was conducted in Syria, Damascus city, in between March and April 2018. Data

collection was conducted online through a web-based questionnaire. This method of data collection

was chosen for several reasons:

• The questionnaire is available to a greater number of people.

• The collected data is very easily exported into SPSS or Excel format.

• Online questionnaire is less expensive than the traditional "paper and pencil", in this study

free web hosting and free domain were used.

The questionnaire form included: (1) instruction for completion, (2) Demographic questions, and

(3) Conjoint questions from an effective experiment plan consist of three steps.

0%

10%

20%

30%

40%

50%

60%

52


The demographic information is shown in table 1& figure 2.

Table 1: Demographics of respondents

Variable Description (ɳ= 75) Percent

(%)

Gender Female 27 36 %

Male 48 64 %

Age

(16-23) Years 25 33.3 %

(24-40) Years 30 40.0 %

More than 40 Years 20 26.7 %

Education

Elementary school or no Education 4 5.3 %

Secondary school 17 22.7 %

University degree or Diploma 40 53.3 %

Higher study 14 18.7 %

Figure 2: Demographics of respondents as pie chart

53


The questionnaire contains three major steps:

First step was through hypothetical question about four tangible and intangible attributes and the

respondents must put a rank from (0-9) to their preferences of those attributes which are (Brand

name, minute price, technical support and queue services) then we calculate the average for each

preference. The brand name was the least preferred through all attributes as shown in figure 3.

Figure 3: Average respondents rank for the four attribute

Second step was through put another hypothetical scenario of third mobile phone company (we call

it X) beside the only two companies in Syria (Syriatel & MTN), then compare that with another

scenario of three prepaid minute hypothetical and real prices (15, 13, 10) S.P. The respondent must

put a rank from (0-10) for this preference for two attribute (brand and price). From these

preferences the part worth utilities were founded by multiplying the preference by 10, and then the

range of attribute utility and the importance for each respondent were calculated as shown in the

table 2.

Table 2:Calculation of attributes importance for one respondent depending on part worth utility

Part worth

utilities Range of utility

attribute Attribute importance

Brand

Syriatel 40

60-30=30 30/120×100%=25% MTN 30

X 60

Price

10 S.P 90

90-0=90 90/120×100%=75% 13 S.P 30

15 S.P 0

Total utility range 30+90=120

After that, the average of Brand importance and the average of Price importance for all respondents

were calculated. The result shows that 25% for Brand importance versus 75% for Price importance,

which means that Syrian respondents were voted for Price more than for Brand names when

4.413333333

7.56

8.52

6.68

0

1

2

3

4

5

6

7

8

9

brand name minute price technical support queue service

54


speaking about importance. The purpose of this step is to test if the Price rate is the main difference

between companies when we try to reduce class consciousness between the two companies by

inserting third one without saying its merits and demerits to the respondents in order to know the

significance of the Brand names and Prices on the preference of Syrian consumer and consequently

for his loyalty.

4. Conjoint analysis

Third step a market simulator have been done, which is usually the most important tools in conjoint

analysis project (Orme, 2010), conjoint analysis (CA) is a methodology in which a decision maker

has to choose from a number of options that vary simultaneously from between two or more

attributes (Green & Krieger, 2001), researchers characterized products or services by set of attribute

values or levels and then measure respondents' purchase interest (Mc Cullough, 2002), this

description presents respondents or judges with several hypothetical products or services, each

consisting of a combination or stimuli of specified features or characteristics (Myers & Mullet,

2003).Such stimuli are therefore described by several attributes. The conjoint results go beyond

attribute importance and provide quantitative measure of the relative appeal of specific attribute

levels (Wyner, 1992). Moreover, CA can be used to change the part worth utilities to something

more useful in management fields or what is called (simulated choice market), which can produce

products or services as scenarios of simulated market. Simulated program will give responding rate

reports for respondents in their choice for the best product or service. This simulator allows the

managers to do (what if) game for investigating different cases like positioning or lunching a new

product or service (Orme, 2010). In our case, it has been used to know the decision of purchase for

Syrian consumers through calculating the average of preferences for respondents (consumers)to

Brand names as intangible attribute and the most preferred tangible attributes (Price and

Coverage),predicted from the first step, for Syrian respondents for the only two mobile phone

companies in Syria(Syriatel, MTN).Based on that, hypothetical multiple scenarios have been made

for the most chosen attribute and changing in one attribute level or more in each scenario on the

bases of conjoint study of sets of utilities formed by these attributes to respondents. These utilities

define the preference of respondents in numerical formula for each levels of attribute. In this case

there were 18 scenarios for two brands (Syriatel, MTN) and two attribute with three levels to each

one, which are hypothetical prices for the prepaid minute at the time of the survey (which is 13

S.P/Minute), to which 3 S.P was added and 2 S.P subtracted, giving the levels (10,13,15

S.P/Minute). Likewise, in Coverage the levels were (good, normal, weak).

4. 1. Conjoint experimental design

Full factorial design was used resulting 18 hypothetical possible combinations for 8 service

attributes (2 Brand×3 Price×3 Coverage). On a scale of 0 to 10 (10 being the best), the respondent

rates each combination. The respondent test results are modified for the regression equation and

then run through the regression. The resulting regression analysis calculates a coefficient for each

independent variable as part of regression output equation. Each coefficient is the measure of value

that respondent (as consumer) places on the service attribute associated with that utility. The table

number 3 provides the choices that respondents had to analysis.

55


Table 3: choices that respondents had to analysis

Card coverage brand Price

1 good A 10

2 good A 13

3 good A 15

4 good B 10

5 good B 13

6 good B 15

7 normal A 10

8 normal A 13

9 normal A 15

10 normal B 10

11 normal B 13

12 normal B 15

13 weak A 10

14 weak A 13

15 weak A 15

16 weak B 10

17 weak B 13

18 weak B 15

The respondent was provided with 18 separate questions, each question contained 18 possible

variations of service attributes. The respondent had to estimate (rate) their overall preference of

each combination of attributes on a scale of 1 to 10 (10 being the best). The average of all

respondents' preferences (choices) were calculated and used to deduce the overall Regression

equation combination preference for Syrian people. Then non-numerical attributes were assigned

numbers in table 4, Brand Syriatel and Good coverage shown as 1's in their respective columns,

Brand MTN and Normal coverage were shown as 2's in their respective columns, weak coverage

was assigned as 3 in its respective column.

56


Table 4: numerical table

Card coverage brand Price Preference

1 1 1 10 4

2 1 1 13 3

3 1 1 15 2

4 1 2 10 4

5 1 2 13 3

6 1 2 15 2

7 2 1 10 5

8 2 1 13 4

9 2 1 15 3

10 2 2 10 5

11 2 2 13 4

12 2 2 15 3

13 3 1 10 8

14 3 1 13 6

15 3 1 15 5

16 3 2 10 7

17 3 2 13 6

18 3 2 15 5

Each individual service attribute is given its own column. Each service attribute now has either the

value of 1 or 0, table 5

The final data preparation step prior to running regression is remove one variable from each set of

variables with more than one choice, removal of these variables removes the predictability of other

variables, which is the problem of Co-linearity of the variables (independent variables or

combination of independent variables should not be able to predict each other), this error conditions

are solved by removing one column of data from each type of variation. So information about

Brand A (Syriatel), weak coverage and price level (10 S.P/Minute) were removed. This has no

effect on the accuracy of regression output, table 6.

57


Table 5: the value 0,1 for each service attribute

Card weak Normal Good A B 10 S.P 13 S.P 15 S.P Preference

1 1 0 0 1 0 1 0 0 4

2 1 0 0 1 0 0 1 0 3

3 1 0 0 1 0 0 0 1 2

4 1 0 0 0 1 1 0 0 4

5 1 0 0 0 1 0 1 0 3

6 1 0 0 0 1 0 0 1 2

7 0 1 0 1 0 1 0 0 5

8 0 1 0 1 0 0 1 0 4

9 0 1 0 1 0 0 0 1 3

10 0 1 0 0 1 1 0 0 5

11 0 1 0 0 1 0 1 0 4

12 0 1 0 0 1 0 0 1 3

13 0 0 1 1 0 1 0 0 8

14 0 0 1 1 0 0 1 0 6

15 0 0 1 1 0 0 0 1 5

16 0 0 1 0 1 1 0 0 7

17 0 0 1 0 1 0 1 0 6

18 0 0 1 0 1 0 0 1 5

Table 6: removing one variable from each set

Card Normal good B 13 S.P 15 S.P Preference

1 0 0 0 0 0 4

2 0 0 0 1 0 3

3 0 0 0 0 1 2

4 0 0 1 0 0 4

5 0 0 1 1 0 3

6 0 0 1 0 1 2

7 1 0 0 0 0 5

8 1 0 0 1 0 4

9 1 0 0 0 1 3

10 1 0 1 0 0 5

11 1 0 1 1 0 4

12 1 0 1 0 1 3

13 0 1 0 0 0 8

14 0 1 0 1 0 6

15 0 1 0 0 1 5

16 0 1 1 0 0 7

17 0 1 1 1 0 6

18 0 1 1 0 1 5

This table is now further prepared for regression analysis, and the removing of these variables did

not affect the output accuracy, these service attributes could still be considered to be part of the

regression equation, put with coefficient of 0.

58


4. 2. Conjoint model

One of the simplest and most commonly used model is the linear additive model, This model

assume that the overall utility derived from any combination of attributes of a given good or service

is obtained as the sum of separate part-worth of attributes. Thus, respondent i's predicted conjoint

utility for profile j can be specified as follows:

�� =��

�

�

�� + ��, � = 1, … , �, � = 1,… . , �(1)

Where K is the number of attributes, Lk is the number of levels of attribute K, and ��is

respondent I's utility with respect to level L of the attribute K, �� is such a {0,1} variable that

equals 1 if profile j has attribute K at level L, otherwise it equals 0.�� , and it is a stochastic error

term .The parameters �� , also known as part-worth utilities, can be used to establish a number of

things. One of them, the value of these parameters indicates the amount of any effect that an

attribute has on overall utility – the larger the coefficient, the greater the impact(kuzmanovic & M.

radosavljevic, 2013). In this paper, Excel format was used to calculate the regression equation

mathematically.

In the Regression equation combination preference (2), which was deduced from table 6 by using

regression test from Excel format, the coefficient attached to each of the service attribute β simply

show the respondents' utility of that attribute. The utilities for each attribute are relative to each

other. For example, Price level (10 S.P) has the highest preference with utility of 0, while Price

level (15 S.P) has the lowest utility of -2.0666.

Also, the overall low significance of the regression F statistic indicates that the regression, overall,

is valid. Each of the variables has low P-value except for Brand B it turns out that it is 0.24, which

means that it is more than the desired significance level (α=0.05), this will lead us to accept Null

hypothesis which says: There is no statistics significant relation between Brand names and Syrian

respondents, and consequently on buy decision for Syrian consumers of mobile phone postpaid

lines. This has supported the results of the First step. Moreover, the regression appears to be good

one because Adjusted R squared is high (close to 1), Adjusted R square= Explained variance over

unexplained variance, here, Adjusted R square is (0.97).

59


Table 7: stastical analysis results for data as shown on Excel sheet

5. Validation of the equation

The respondents rated the combination of attributes on card 13 with an 8, as shown in table 6. From

Equation (1), the predicted combination preference for card 13 attribute combination is:

� = 4.167 + 1(�1) + 3.167 � (�2) + 0.11(�3) + 1.17(�4) + 2.167(�5) (3)

X1, X3, X4, X5 = 0, X2= 1, as shown in table 6 when:

X1: Normal coverage

X2: Good coverage

X3: MTN

X4: 13 S.P

X5: 15 S.P

From equation (3):

� = 4.167 + 1(0) + 3.167(1) + 0.11(0) + 1.17(0) + 2.167(0) � = 4.167 + 3.167 = 7.34

Here we have 7.34 which are very close to consumers' rating of 8 for the scenario 13, and the

resulting regression equation (2) still does a good job of predicting overall preference.

6. Conclusion and recommendations

This paper was aiming to use the conjoint analysis method to estimate and find a specific way to

know how Syrians think when they choosing a mobile postpaid brand, i.e. what is it particularly

that make them choose between a specific operator company and the preferred services or attributes

of a competing operator company, also to measure the nominal value of brand names as an

intangible assets of the company.

Regression Statistics

Multiple R 0.992771

R Square 0.985594

Adjusted R 0.979592

Standard Er 0.235702

Observation 18

ANOVA

df SS MS F Significance F

Regression 5 45.61111 9.122222 164.2 1.28632E-10

Residual 12 0.666667 0.055556

Total 17 46.27778

Coefficientstandard Erro t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 4.166667 0.136083 30.61862 9.24E-13 3.870167796 4.463166 3.8701678 4.46316554

Normal 1 0.136083 7.348469 8.87E-06 0.703501129 1.296499 0.7035011 1.29649887

good 3.166667 0.136083 23.27015 2.36E-11 2.870167796 3.463166 2.8701678 3.46316554

B -0.11111 0.111111 -1 0.337049 -0.353201426 0.130979 -0.3532014 0.1309792

13 S.P -1.16667 0.136083 -8.57321 1.84E-06 -1.463165538 -0.870168 -1.4631655 -0.8701678

15 S.P -2.16667 0.136083 -15.9217 1.96E-09 -2.463165538 -1.870168 -2.4631655 -1.8701678

Regrission Equation Combination Preference=4.167+1×(Normal)+3.167×(Good)+(-0.11)×(B)+(-1.17)×(13 S.P)+(-2.167)×(15 S.P)

60


Conjoint analysis implementation should be done again after a certain period of time because

respondents (users) preferences change over time (kuzmanovic & M. radosavljevic, 2013).

Moreover, intangible assets (like Brand names) should be measured in the same way in Syria in

order to achieve two goals: (1) measured intangible assets are easier to manage and understood and

can be useful to perform successful marketing and brand strategy based on the Syrians' need, (2)

these intangible assets give commercial profit for the owned company when managed well.

7. References BUDDE. (2014). BUDDE comm. Retrieved from Paul budde communication bty LTD web site:

http://www.budde.com.au/Research/Syria-Telecoms-Mobile-Broadband-and-Forecasts.html

Churchill, G., & Lacobucci, D. (2002). Methodoligal foundation in marketing research (8 ed.).

Londen: Harcourt Publishing.

Ernest, N., & Retha, D. (2002). The use of conjoint analysis to determine consumer buying

preferences: A literature review. Journal of family Ecology and consumer, 30.

Green, P., & Krieger, A. a. (2001). Thirty years of conjoint analysis:reflection and prospects.

interfaces, 31, pp. s56-s73.

Hair, J., & Black, W. (1995). Multivariate Data Analysis. Englenwood cliffs,NJ, Prentice Hall.

Head, M. (2010). understanding student attitude of mobile phone applications and tools: A study

using conjoint,cluster and SEM Sustem. Proceedings of the 18th European conference on

information Systems (ECIS). Pretoria, southe africa.

Hensher, D. (2001). the valuation of commuter travel time saving for car drivers: Evaluating

alternative model specifications. transportation, 101-118.

ITU. (2018). www.itu.int. Retrieved 2014, from ITU: www.itu.int/en/ITU-

D/stastics/facts/ICTFACTsFigures.e.pdf

jeanselme.M, W., & Reyolds.J. (2006). The advantages of preference-based segmentation:an

Investigation of online grocery retailing. 14(4), 297-308.

kim, C. C. (2008). a Systematic approach to new mobile service creation. Expert System with

applications.

Kim, Y. (2004). Estimation of consumer preferences on new telecommunications services:IMT-

2000 service in Korea. Information Economics and Policy, 73-84.

kuzmanovic, M., & M. radosavljevic, M. V. (2013). understanding student preferences for postpaid

mobile services using conjoint analysis. Acta Polytechnica Hungarica, 10, 159-176.

Kuzmanovic, M., & Martic, M. (2012). an approach to competitive Product line Design using

conjoint analysis.

61


Kuzmanovic, M., & Martic, M. (2012). Using conjoint analysis to create superior value to

customers. Metallurgia international, 17(2), 93-99.

Kuzmanovic, M., Vujosevic, M., & Martic, M. (2012). using conjoint analysis to elicit Patients'

preferences for puplic primary care service in Serbia. HealthMED, 6(2), 496-504.

Mc Cullough, D. (2002). A user's guide to conjoint analysis. marketing research, 14(N2), pp. 18-

23.

Myers, J. a., & Mullet, G. (2003). Managerial applications of multivariate analysis in marketing.

American marketing association.

Nakamura, A. (2010). Estimating switching costs involved in changing mobile phone carriers in

Japan:Evaluation of luck-in factors related to Japan's SIM card locks. Japan:

telecommunications Policy.

Neal, W., & Strauss, R. (2008). Value creation:the power of brand equity. (I. M. Inc., Ed.) 5191

Natorp Boulevard Mason, Ohio, USA: Texere,an imprint of Cengage Learning.

Nicola, N., & farraj, O. A. (n.d.). Hospital patient migration:Analysis using a utility index. 293-

316.

Obradovic, T., & Kuzmanovic, M. (2010). The role of conjoint analysis in the new product price

sisibility research. Journal for theory and practice management, 15, 51-58.

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

research (second edition ed.). Midson, Wis: Research Publisher.

P.Kotler, W. P. (2006). B2B Brand Management. Germany: Springer.

Popovic M., K. M. (2012). Using conjoint analysis to elicit employers' preferences toward key

competencies for a business manager position. Journal for theoty and practice management,

17(1), 17-26.

Sobolewski, M., & Czajkowski, M. (2012). network effects and preference heterogeneity in the

case of mobile telecommunications market. telecommunications Policy, 36(3), 197-211.

Sohn, S., & Ju, Y. (2010). Conjoint analysis for recruiting high quality students for college

education. Expert Systems with applications.

Statista. (2014). Statista corporation. Retrieved from Statista corporation web site:

www.statista.com/statistcs/218605/global-mobile-service-revenue-since-2010/

Wyner, G. (1992). Use and limitations of conjoint analysis-PartI. Marketing research, 4(N2), pp.

42-44.

Documents

Suggested Model to measure the Value of Syrian Mobile ... · 4. Conjoint analysis Third step a market simulator have been done, which is usually the most important tools in conjoint