Management of telecommunication operator services in Serbia – Case

study Eastern Serbia

BILJANA STOJAN ILIC

Faculty of Management Zajecar

Megatrend University of Belgrade

Boulevard of Marshal Tolbukin 8, Belgrade

SERBIA

SAFWAN A. SALAIMEH

Dean of Information Technology Faculty,

Aqaba University of Technology

Aqaba, JORDAN

Abstract: The development of telecommunication technology in the last three or more decades has

created the conditions for high quality and high speed data transmission between physically separated

devices (central computers, terminals, mobile devices). Telecommunication can be defined as the

electronic connection of physically (geographically) remote computers, and the telecommunication

system as a component of compatible telecommunication devices connecting physically separated

devices that can transmit text, image, audio and video information. The paper focuses on the choice of

telecommunication providers in Serbia. It examines the attitudes of service users and what influences

their choice of a particular mobile network. Consumer attitudes can influence the improvement of

service management of mobile operators in Serbia. The paper represents a modest contribution to the

use of telecommunication technologies to improve the existing offer on the market of mobile operators

in Serbia and especially in the eastern region of the country. The city of Zajecar, the largest city in the

eastern region of Serbia, is included in the study as a representative example.

Keywords: Telecommunications, Technology, Mobile Operators, Service Management, Consumers, Markets,

Serbia, Eastern Serbia.

Received: June 14, 2021. Revised: January 24, 2022. Accepted: March 15, 2022. Published: March 29, 2022.

1 Introduction

Special systems are used for the transmission of

telecommunication signals or messages, which may

consist of wires or be based on radio, optical or

electromagnetic waves [1]. "Communication - the

exchange of information - is essential both for the

social life of mankind and for the organization of

nature" [2]. The term telecommunication defines the

field of human activity that has the task of

transmitting signals, messages, words, signs, records,

images and sounds or the transfer of information

between two or more remote users [3]. Is it possible

to "improve" the services of new technology? What

else can be offered to the users of services in the field

of modern telecommunication systems? Is it possible

to improve the business of mobile operators in the

growing global market and in what way? These are

some of the questions that the author will try to

answer by making a modest contribution in the part

related to the management of telecommunications

services. This study aims to highlight common

characteristics and problems related to the attitude of

consumers of mobile telecommunication

technologies, using the current mobile operators in

Serbia as an example. The study highlights how to

improve mobile services in Serbia, i.e. how to

improve service management.

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The word telecommunications consists of the

Greek word tele (Greek: τηλε), which means far, and

the Latin word, communicare, which means to share.

[4]. Telecommunication systems have the following

functions: Establishing a connection and transferring

information between sender and receiver;

determining the direction of message flow in the most

efficient manner; performing the most basic

information processing to ensure that the correct

message reaches the correct receiver; controlling

possible errors and controlling the flow of

information; converting message transmission at one

speed (e.g., computer speed) to the speed that a

communication line can achieve [5][6]. Signals

carrying different information over communication

networks can be represented as analog and digital.

The analog signal is represented as a continuous line

so that the positive voltage is represented by a + 1 and

the negative by 0 [7]. The baud rate is measured in

bits per second by default. Since a digital signal is

more discrete than a continuous system, it must be

translated to an analog signal if you want to transmit

it over an analog system [8][9]. For example, if it is

necessary to transmit data over telephone lines that

operate on analog signals, the conversion from digital

signals to analog must be done [10]. However, the

development of new technologies and innovations

has primarily contributed to the emergence of new

connections between people, a new modern business

and the environment in which business takes place.

The modern business environment is very

dynamic, unpredictable, turbulent, characterized by a

shorter life cycle of products or services, by a

pronounced competition on a global scale, by the

transition of society from an industrial society to a

society where knowledge is treated like a critical

resource in business - by the volatility of the market

and economic conditions [11]. Cellular mobile

communications, or cellular telephony, is the

technology with the fastest exponential growth. Since

1982, when the first cell phones appeared, and until

today, mobile devices are used by more than 5 billion

people on the planet or a little more than 70% of the

world's population [12]. The constant evolution of

data transmission technology has produced several

generations of mobile systems, some of which are

divided into subcategories. The four basic

generations are 1G, whose development began in

West Germany in 1972, 2G, better known as GSM

(Global System Mobile Communications), 3G or

UMTS (Universal Mobile Telecommunications

Systems), 4G, and 5G, which is still under

development and whose standard is based on needs

[13]. Codecs based on different Quality of Service

(QoS) requirements are used. One of the most

important QoS requirements is that packets are

transmitted in real time through the network. The

one-way transmission time or end-to-end (ETE)

packet delay and the packet delay fluctuations or

jitter must be below the threshold values. [14].

2 Problem Formulation -

Telecommunication Operators in

Serbia

The first mobile operator in Serbia was Mobtel

(short for Mobile Telecommunications), founded in

April 1994 by the Serbian state-owned company PTT

(Post, Telephone, Telegraph) and BK Trade, a private

company from Russia. According to a survey

conducted by the Regulatory Authority for Electronic

Communications and Postal Services (RATEL),

there are currently 8.65 million active cell phone

users in Serbia, which is more than the population

[16]. Today, mobile phone services are provided by

three companies:

- Telecommunications Company Telekom Srbija

Joint Stock Company, Belgrade; 58.11% owned by

the Republic of Serbia, 20% owned by Telekom

Srbija, 14.95% owned by citizens of the Republic of

Serbia and 6.94% owned by current and former

employees of Telekom Srbije a.d. and its

predecessor.

-Telenor DOO (limited liability company)

Belgrade; (until March 1, 2022 - 100% owned by

"PPF TMT Bidco 1 BV", a company from the

Netherlands). The ownership of the company was

changed, as well as the name of the company, and

Telenor was renamed Yettel (on Murch, 2022).

However, in this paper, the name Telenor is used, as

the investigation was conducted before the upcoming

changes.

-A1 Serbia DOO Belgrade; 100% owned by

Mobilkom CEE Beteiligungsverwaltungs GmbH of

Austria [17]. All three operators have individual

licenses for the use of radio frequencies based on

public tenders (licenses) for the public mobile

network and public mobile network services on a

technology-neutral basis. Licenses have also been

awarded to two mobile virtual operators: Globaltel

and Mundio Mobile d.o.o.

The operators use GSM (2G), UMTS (3G) and

LTE (4G) technology. In this paper, research is

conducted for the first three mobile operators:

Telekom, Telenor and A1. Operators: Globaltel and

Mundio Mobile are still in their infancy and do not

have a sufficient presence on the mobile

communications market in Serbia.

2.1 Eastern Serbia - The city of Zajecar

The author conducted the research in the eastern

part of Serbia, more precisely in the area of the city

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2 Literature review

of Zajecar. The research covered 3 smaller areas, i.e.

villages belonging to the region of the town of

Zajecar, including the population of Zajecar itself.

Zajecar is the administrative center of Zajecar district

and also one of the largest cities in Eastern Serbia.

According to the last census (2011), the city had

about 44,000 inhabitants [18]. In 2011, for the first

time, several suburban settlements moving towards

Zajecar were included in the urban settlement:

Grljan, Zvezdan, Veliki Izvor. These are places that

are also covered by the research. The town is located

in the Zajecar valley on the Veliki Timok river.

Zajecar is located between two mountain ports -

Carpathians and Balkans, and this part of Serbia is

classified as rural part of the country [19]. Given this

geographical location of the town - the valley

between the two mountains - mobile operators often

face problems with the installation of appropriate

equipment (relays) for mobile network coverage in

the whole area. The Serbian Regulatory Authority for

Electronic Communications and Postal Services

conducted the benchmark of mobile networks in

Serbia in 2021 (in the months of September, October

and November). Then, the values of quality

parameters were measured and calculated, as well as

the overall result showing the best

telecommunication network in Serbia [20].

According to the performance rankings of mobile

networks, which include: Voice service with 40%

share in the overall result; Web browsing with

22.80% share in the overall result; Data transmission

services with 15% share in the overall result;

YouTube with 13.20% share in the overall result; The

messaging service (via WhatsApp application) with

9% share in the overall result, it was evaluated by the

users that the best quality of service is provided by

the mobile operator "Telekom Srbija" (with a score

of 89.7 points), in second place is Telenor and in third

place is A1 [21]. What this research lacked was a

detailed analysis of all regions in Serbia, especially

the less developed rural regions, which include the

region of Eastern Serbia, the city of Zajecar.

The author decided to study the attitudes of

consumers of mobile operators in the market of the

eastern part of Serbia, in the city of Zajecar, because

the city has a very characteristic demographic

structure (mostly 45 years old), but also lower

incomes (salaries) of employees. compared to other

regions of the country [22]. There is a pronounced

regional inequality in Serbia, and the western regions

of the country are much stronger economically and

socially than the eastern and southern parts of the

country [23] [24].

2.1.1 Methodology

The paper applied the method of field research, since

the secondary data were not sufficient to shed light

on the phenomenon of the use of telecommunications

providers in the Eastern part of the country. The

method of interviewing consumers of mobile

operators was used and for this purpose an

anonymous questionnaire was prepared. A total of

241 randomly selected individuals were included in

the study.

Of the seven questions, four were general in

nature and related to gender, age, education level, and

occupation. The last three questions of the survey

were comprehensive in nature and related to the

services provided by the mobile service providers.

The questions were phrased as follows: a) Which of

the three official mobile networks in Serbia (Telenor,

Telekom and A1) do consumers use; b) On a scale of

1 to 5 (1 - not important, 2 - less important, 3 -

important and not important, 4 - important, 5 - very

important), respondents were asked to rate how

important the following services provided by

telecommunications providers are: Speed, package

price; good network coverage; Internet; the

possibility to buy a mobile device in installments - a

cheap purchase; c)The next question was related to

the level of satisfaction with your mobile operator,

according to the following ratings and descriptions: 1

- Very dissatisfied; 2 - Dissatisfied; 3 - Neither

(average); 4 - Satisfied; 5 - Very satisfied; d) Finally,

respondents were asked to express their opinion

about the improvement of the mobile network and

telephone service in Serbia, using a free formulation.

The statistical method of multiple logistic regression

was used for data processing in the study, but also the

descriptive method. Multiple logistic regression is

used when the researcher models a predictive

relationship between one or more independent

variables and more than one binary dependent

variable, in other words, logistic regression models

can be useful when the dependent variable is not

binary and/or the categories are not ordered or

arranged [25]. The formulas for calculating multiple

logistic regression are represented by the numbers 1,

2, and 3.

𝑙𝑛 𝑃𝑟⁡

(𝑌𝑖 = 1)

𝑃𝑟⁡

(𝑌𝑖 = 𝐾)= 𝛽1 ∙ 𝑋𝑖(1)

𝑙𝑛 𝑃𝑟⁡

(𝑌𝑖 = 2)

𝑃𝑟⁡

(𝑌𝑖 = 𝐾)= 𝛽2 ∙ 𝑋𝑖(2)

𝑙𝑛 𝑃𝑟⁡

(𝑌𝑖 = 𝐾 − 1)

𝑃𝑟⁡

(𝑌𝑖 = 𝐾)=𝛽𝑘 −1∙𝑋𝑖(3)

Where Xi is the vector of explanatory variables

describing observation i, βk is a vector of weights

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(or coefficient regression) corresponding to

outcome k, and score (Xi, k) is the score associated

with assigning observation i to category k. In discrete

choice theory, where observations represent people

and outcomes represent choices, the score is

considered the utility with a person i choosing

outcome k. The predicted outcome is the one with the

highest score [26].

The aim of the research was to predict the

behavior of mobile service users for three operators

on the Serbian market based on the following

predictors or regressors: gender, age, occupation, and

education level. For this part of the research, a

descriptive method was used in addition to the

multiple logistic regression method.

3 Problem Solution

Of the total 241 respondents, 111 respondents

were female, while 130 respondents were male. The

number of people by age ranged from 18 to 70 years

old - with most people being over 45 years old (183).

By occupation, most respondents were retired (91),

followed by employees in education, law, or another

public agency (82), health care workers (22), and the

fewest unemployed (15).

By education level, most respondents had a

secondary or higher education degree - 185

respondents, while 43 respondents had a university

degree, completed university, or had a master's

degree. Only 15 respondents had completed primary

education. The parameters related to the offer of

operators in Eastern Serbia: Telenor, Telekom, and

A1 belong to the I (first) variable, i.e., the basic

research variable.

Table 1 shows how many of the respondents use

the mobile operators in Eastern Serbia. 65

respondents voted for Telenor, 82 for Telekom, and

94 for A1. In preparing the analysis, the users of A1

operators (group III) were taken as the reference base

(or base category) against which the other two groups

were compared. Another category could also be taken

for the reference group, but not because other

comparisons made no sense, as most respondents

voted for the A1 mobile network.

Table 1 Structure of surveyed respondents by

mobile operator

Number

Percentage

Operator

Telenor

27,0%

Telekom

34,0%

39,0%

Valid

241

100,0%

Source: author's research

Since the attitudes of respondents from the eastern

part of the country differ from those of respondents

throughout Serbia, the author wanted to investigate

the main reasons for using the mobile operator A1 -

over Telenor and Telecom. The mobile operators

were considered with the following parameters of the

respondents: Age, Education and Prices.

Table 2 shows "Model Fitting Information" -

includes a likelihood ratio chi-square test that

compares the full model (i.e., that includes all

predictors) to a null model (or a model that considers

only the intercept) [27].

Statistical significance indicates that the full

model represents a significant improvement in fit

over the null model, as you can see in Table 2

[χ²(16)=222.515, p<.001].

Table 2 Model Fitting Information

Model Fitting

Criteria

Likelihood Ratio Tests

-2Log

Likelihood

Chi-

Square

Sig.

Intercept

Only

527,540

Final

305,026

222,515

0,000

Source: author's research

Table 3 - "Goodness of Fit" - includes the

Deviance and Pearson Chi-Square tests, which are

useful for determining whether a model has a good fit

to the data. Non-significant test results are indicators

that the model is a good fit to the data [28][29].

According to Field and Petrucci (Field and Petrucci

did not always necessarily agree), the result is mixed,

based on the example for social researchers of how to

perform multinomial logistic regression [30].

Table 3 Goodness-of-Fit

Chi-Square

Sig.

Pearson

296,764

448

0,000

Deviance

303,639

448

1,000

Source: author's research

Pearson’s chi-square test indicates that the model

does not fit the data well (χ²(448)=296.764, p=0.000),

whereas the Deviance chi-square does indicate good

fit (χ²(448)=303.369, p=1.00)

Table 4 Pseudo R-Square – analogni parametri

koeficijetu determinacije

Cox and Snell

0,600

Nagelkerke

0,676

McFadden

0,421

Source: author's research

Table 4 shows pseudo R-squared values treated as

rough analogs to the R-squared value in OLS

(Ordinal Logistic) regression. In general, there is no

clear guidance in the literature on how these values

should be used or interpreted [31]. The explained

variability is above 50%.

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Table 5 - Likelihood Ratio Tests - presents the total

contribution of all variables (regressors) with respect

to the dependent variable (all operators) from the

study - includes the total contribution of each

independent variable in the model. Using the

conventional statistical error threshold of 005% in the

model, the table shows that age, school, and price are

the most important predictors (regressors) in the

model.

In other words, these variables are the determining

factors in choosing a cellular service provider. Thus,

from the perspective of mobile operators, it is

possible to plan future marketing and management

strategies in the market of telecommunications

technology in Eastern Serbia. Other variables have no

statistical significance and are therefore not

considered.

Table 5 Likelihood Ratio Tests

Effect

Model Fitting Criteria

-2 Log-Likelihood

of Reduced Model

Sig.

Intercept

330,700

0,000

Age

454,751

0,000

Job (Occupation)

307,361

0,311

Education

324,324

0,000

Speed

306,760

0,420

Price

331,728

0,013

Coverage

307,277

0,324

Internet

325,564

0,093

Purchase option

305,402

0,829

Source: author's own research

Table 6 The final result of Multiple regression

analysis

Mobile Operator

Sig.

Exp(B)

Telenor

Intercept

10,584

0,000

Age

-0,463

0,000

0,629

Occupation

0,234

0,498

1,264

Education

1,498

0,000

4,472

Speed

0,490

0,251

1,632

Price

-0,422

0,005

2,025

Coverage

0,787

0,154

2,197

Internet

-,430

,528

,651

Purchase

0,211

0,645

1,235

Telekom

Intercept

6,843

0,000

Age

-0,221

0,000

0,802

Occupation

0,371

0,144

1,450

Education

0,426

0,097

1,531

Speed

0,408

0,216

1,504

Price

-0,425

0,090

0,883

Coverage

0,217

0,585

1,242

Internet

-,342

,488

,710

Purchase

0,215

0,543

1,240

Source: author's own research

The results from Table 6 represent comparisons

between each group of respondents determined for

one operator with the reference category (group III),

ie the users of the mobile operator A1. In particular,

the regression coefficients show which predictors -

regressors (age, education, price) significantly

differentiate the groups of respondents - determined

users of Telenor and Telekom mobile networks, from

the respondents who are determined for A1 mobile

operator. The B column contains regression

coefficients (expressed in the metric of log-odds).

The Exp(B) column contains odds ratios [28] [29]

[31].

The first set of coefficients represents

comparisons between A1 users (Group III in Table 1)

and those who are Telenor users (Group I in Table 1).

Significant predictors (regressors) were: age,

education (B = -0.463, p <.001), and price in the

model. From the analysis, it can be concluded that

younger people opted more for Telenor mobile

operators (average age is 42; standard deviation is

11.7 compared to the range of 20 to 70). The result of

0.629 indicates that for each unit in the variable "age"

- when it increases - the chances of using the mobile

operator Telenor decrease and increase the chances

of using the A1 mobile operator. In other words, the

older the respondents (over 42 years old), the more

they opt for the A1 mobile operator.

When it comes to school regressors, a positive

regression coefficient is evident, which can be seen

in Table 6. Respondents with higher education opted

for the mobile operator Telenor, while respondents

with lower education opted for mobile operator A1.

In other words, almost 4.5 (4,472 from Table 6) times

more likely to be more educated respondents to opt

for the mobile operator Telenor (B = 1,498, p <.001).

The third significant predictor or regressor in the

model was the price of services, especially to the

mobile operator Telenor (B = 0.422, p <.005).

Respondents gave a poor rating for the price of

services of the mobile operator Telenor (slope

regression parameter has a negative sign). The odds

ratio of 2,025 from Table 6 indicates that for each unit

of price dissatisfaction increases, the odds are

reduced by two times when choosing a Telenor

operator, and the same is increased for choosing an

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A1 operator. In other words, the price was the

deciding factor for the choice of using the mobile

operator A1 in Eastern Serbia.

The situation with the mobile operator Telekom

was similar to that with Telenor (except for the price,

because this regressor was not significant with

Telekom, ie - logically - it was in the middle, between

A1 and Telenor). Thus, from the analysis presented

in Table 6, it can be concluded that younger people

opted for Telecom more (average age is 43; standard

deviation is 11.8 compared to the range of 20 to 70).

The result of 0.802 indicates that for each unit in the

variable "age" - when it increases - the chances of

using Telecom decrease and increase the chances of

using A1 mobile operator. That is, the older the

respondents (over 43 years old), the more they opted

for the services of the mobile operator A1.

The regressor of school readiness also had a

positive regression coefficient, in Table 6.

Respondents with a higher level of education opted

for the Telecom mobile operator, while respondents

with a lower level of education opted for the A1

operator. That is, almost 2 (1,531 from Table 6) times

more likely to be more educated respondents to opt

for Telecom (B = 0.426, p <.001) than for operator

A1 and vice versa, twice as likely to be less educated

respondents to opt for mobile operator A1 to

Telekom mobile operator.

Table 7 Classification table

Observed

Predicted

Telenor

Telekom

Percent

Correct

Telenor

78,8%

Telekom

77,7%

84,3%

Overall

Percentage

28,0%

39,5%

32,5%

80,2%

Source: author's research

Table 7 presents the classification of statistics,

which is used to determine which group of

respondents is most suitable for predicting results

(representative group). The table shows that this is

the group of respondents who use the mobile network

of operator A1, or group III (in Table 1) - the largest

percentage (80.2%).

The author points out the following observations.

According to the parameters taken into consideration,

the respondents estimated that the best mobile

network in the Eastern part of Serbia, in the territory

of the city of Zajecar - A1 is a mobile operator, while

Telecom is in third place. There are several logical

explanations for why the respondents from the

Eastern part of Serbia are not as determined to use a

mobile operator as the respondents in the entire

territory of Serbia (who stated that the mobile

operator Telekom is the best).

The older respondents in the territory of the city

of Zajecar, in Eastern Serbia, are mostly pensioners,

who following their pensions use the most favorable

services, that is - for them the price is a crucial factor

when choosing a mobile network. Operator A1 offers

the most favorable packages and services within

them, with unlimited calls to all networks and

messages, which is very important for retirees. Such

packages are the cheapest with the A1 operator.

The demographic structure of the city of Zajecar,

with the surrounding nearby places, has more

characteristics of the older population (average age of

the population in the city of Zajecar - 47 years).

Therefore, this result is not surprising. However, the

younger population, which in the minority opted

more for the other two mobile operators - Telenor

(recently Yettel) and Telecom, mostly due to good

network coverage. Members of the younger

population travel more, and the availability of the

network at all times is the most important parameter.

Respondents with higher education were more in

favor of using the services of mobile operators

Telenor and Telekom, mostly due to the good share

of the Internet within the mobile package. Namely,

this group of respondents pointed out the importance

of connection to the Internet, because they mostly use

the services of the Internet (e-mail) when

communicating.

4 Conclusion

Summarizing the results of research and attitudes

of users of telecommunications operators in Eastern

Serbia, the main recommendation is for mobile

companies - to do good market segmentation

throughout Serbia - to offer a more complete service

depending on the region and attitudes of users, age,

and other criteria. Asked about the degree of

satisfaction of service users with the mobile operator,

the results of users are far more in favor of the mobile

operator A1. Out of a total of 94 (Table 1)

respondents who are also users of the A1 mobile

operator - as many as 92 respondents rated the

services of this mobile network with a score of 5 - ie

they gave the highest score to the question regarding

satisfaction with the mobile network they use. Also,

this group of respondents had the least objections in

the last question from the survey, which referred to

the way of improving the service of the mobile

operator. In contrast to this group, respondents who

opted for Telenor mobile operator (65 users - Table

1), in most cases were very dissatisfied with their

mobile network. When asked how to improve the

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service, the answers were mainly based on - reducing

the price of the package, with an additional increase

in the Internet tariff. Given that market segmentation

takes place through rough and fine segmentation,

depending on the product or service offered, for

mobile operators operating in Serbia, it would be

desirable to apply fine segmentation, especially when

examining regional markets within the country.

Fine segmentation would represent a complex

description of segments (product functions, package

form, groups of mobile devices offered on the market

within the package, with the possibility of purchasing

in several installments). Therefore, mobile operators

should pay more attention to the specialization of

products - mobile packages, in several different

market segments. Greater attention should also be

paid by mobile operators in Serbia to the

specialization of the market, in terms of meeting

more diverse needs, for certain groups of consumers.

This recommendation could be particularly relevant

to the region of Eastern Serbia.

Product characteristics are considered to be more

conducive to market segmentation than consumer

characteristics. However, when the case is viewed

from the point of view of mobile operators in Serbia,

market segmentation should be done within the

prices of mobile service packages, anticipating

market potential, by defining the appropriate

marketing mix for each market segment.

Analyzing the factors of competition,

telecommunications mobile operators in Serbia could

predict their market share in each segment, make a

cost-benefit assessment, in other words, determine

the "benefit" in terms of income following the

company's goals, ie mobile companies.

References:

[1] Constitution and Convention of the International

Telecommunication Union, Annex, Geneva,

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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.85

Biljana Stojan Ilic, Safwan A. Salaimeh

E-ISSN: 2224-2899

984

Volume 19, 2022