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Finance, Accounting and Business Analysis 
Volume 6 Issue 2, 2024 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2024.2.11 

 

Does Urban Fixed-Line Telecommunication Density Influence 

Profitability and Operational Efficiency in Greece's Telecommunications 

Industry? 

 

Emmanouil Taxiarchis Gazilas   
Department of Economics, University of Piraeus, Greece  

 

 

Info Articles   Abstract 

 

History Article: 

Submitted 29 October 2024 

Revised 9 December 2024 

Accepted 13 December 2024 
 

 Purpose: This study investigates the influence of Urban Fixed-Line 

Telecommunication Density on financial performance in Greece's 

telecommunications industry, focusing on the nation’s three leading 

telecom companies. 

Design/Methodology/Approach: Using a balanced panel dataset 

covering a ten-year period, the study employs fixed and random effects 

regression models to assess the impact of Urban Fixed-Line 

Telecommunication Density on key financial indicators. The 

Hausman test is applied to identify the most suitable model for each 

metric, while the Breusch-Pagan test evaluates the presence of 

heteroscedasticity. 

Findings: The results reveal a substantial relationship between Urban 

Fixed-Line Telecommunication Density and financial performance, 

with fixed effects proving more suitable for certain indicators and 

random effects for others. Potential heteroscedasticity detected in 

several models suggests the need for robust estimations.  

Practical Implications: This study underscores the importance of 

telecommunications infrastructure in supporting financial growth and 

operational efficiency, providing insight for policymakers and industry 

leaders on prioritizing infrastructure improvements. 

Originality/Value: The research offers a unique perspective on the 

role of fixed-line telecommunication density in enhancing financial 

performance in a liberalized, competitive market, filling a gap in 

telecommunications and infrastructure research in Greece 

Paper Type:  Research Paper  

 

Keywords: 

Telecommunications, 

Financial Performance, 

Urban Density, Fixed-Line 

Services, Greece 
 

 

 

JEL: L96, L25, G30  

   

Address Correspondence:   

E-mail: mgazilas@unipi.gr  

 

 

 

  

https://doi.org/10.37075/FABA.2024.2.11
mailto:mgazilas@unipi.gr
https://orcid.org/0009-0003-0554-500X


Emmanouil Taxiarchis Gazilas / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

 

229 

 

INTRODUCTION 
 

Telecommunications infrastructure is becoming more and more important in the digital age, 

influencing social connections and economic growth. Even though mobile technologies are gaining 

popularity faster than fixed-line telecommunications, fixed-line services are still an essential part of urban 

infrastructure, especially in developed nations like Greece. The extent to which fixed-line 

telecommunication services are prevalent in urban areas is a measure of how widely available and accessible 

communication technology is—a necessity for households, businesses, and government operations alike. To 

better understand this aspect of urban infrastructure, the Urban Fixed-Line Telecommunication Density 

(UFLTD) has been calculated, a key metric that captures the relationship between fixed telephone 

subscriptions and the urban population. By estimating this ratio (UFLTD = fixed telephone subscriptions / 

urban population), research aims to quantify the extent to which fixed-line infrastructure is deployed across 

Greece’s cities. This calculated ratio serves as an essential indicator of the fixed-line network's spread and 

helps measure how effectively telecommunication companies reach urban populations. 

Greece's economic and social progress has long been based on the telecommunications sector, whose 

evolution reflects broader global trends. As a historical and infrastructure statistic, Urban Fixed-Line 

Telecommunication Density (UFLTD) has been essential to comprehending the connection between 

economic outcomes and telecommunications infrastructure. UFLTD offers a strong lens through which to 

examine the financial performance of major industry participants, especially in Greece, a nation 

distinguished by its distinct urbanization patterns, regulatory changes, and economic difficulties. But as the 

telecommunications industry changes, it's unclear if UFLTD is enough to fully capture the range of its 

effects, particularly in light of innovative advancements like satellite-based technologies. Greece's historical 

reliance on fixed-line infrastructure reflected its attempts to upgrade and incorporate the telecoms framework 

of the European Union. UFLTD is an essential component of this journey, especially in cities where 

economic activity and productivity are driven by connectivity. UFLTD is a significant indicator in urban-

centric studies like this one because the growth of fixed-line networks represented advancement and 

modernization for Greek telecom behemoths like OTE, Vodafone, and Wind. According to our 

investigation, UFLTD showed strong relationships with financial performance indicators, confirming its 

ongoing importance in cities where dependable, fast connections are still necessary for both homes and 

enterprises. 

However, the conventional dominance of fixed-line networks is being challenged by the emergence 

of cutting-edge satellite systems, such as those offered by Starlink. The future of telecommunications is 

represented by these technologies, which are intended to address connectivity gaps in isolated and 

underserved areas. Greece's geographic characteristics, such as its islands and rugged terrain, make it a prime 

target for using satellite technology to supplement current networks, even though the country has historically 

placed a high priority on urban connectivity. This change forces a reassessment of UFLTD's suitability as 

the exclusive measure of telecoms advancement. In spite of this, UFLTD is still significant in the Greek 

setting. It offers a historical standard by which new technologies may be evaluated, reflecting the history and 

effects of urban connection. Future studies must, however, incorporate contemporary metrics like satellite 

connections, mobile data usage, and broadband adoption in order to stay forward-looking. The interaction 

between established and new technologies in influencing financial performance and economic outcomes 

would be more accurately captured by this more comprehensive framework. 

Developing an understanding of the connection between urban fixed-line communications density 

and telecom businesses' financial success might help one better understand the dynamics of the industry as 

a whole. Companies that offer fixed-line telecommunication services must modify their business strategies 

to satisfy the growing need for dependable and quick communication services from urban populations while 

preserving profitability. The three biggest telecom providers in Greece—COSMOTE, VODAFONE, and 

WIND (since merged with Nova)—are the subject of this paper. These companies control almost 80% of 

the telecom sector and hold a significant market share in both mobile and fixed-line services, making them 

the leading enterprises in the Greek market. 

The Greek telecom industry experienced several challenges in the past decade, such as the global 

COVID-19 pandemic and the financial crisis that overtook the nation from 2009 to 2018. Fixed-line 

telecommunication services have remained crucial in despite these challenges, especially in cities where 

households and businesses continue to depend on reliable communication lines. Since fixed-line 

infrastructure is frequently used to supply broadband internet services, the density of these lines is critical to 

the financial sustainability of telecommunications companies. The primary objective of this paper is to 

examine the correlation between COSMOTE, VODAFONE, and WIND's financial performance and 

changes in Urban Fixed-Line Telecommunication Density (UFLTD) between 2013 and 2022. This study 

intends to evaluate how changes in Urban Fixed-Line Telecommunication Density over time have affected 

these companies' profitability by concentrating on important profitability ratios like Return on Equity 



Emmanouil Taxiarchis Gazilas / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

 

230 

 

(ROE), Return on Capital Employed (ROCE), Gross Profit Margin (GPM), Operating Profit Margin 

(OPPR), and Net Profit Margin (NPM). These ratios offer a thorough understanding of a company's capacity 

to make money from operations, equity, and capital, and are generally recognized as crucial markers of its 

financial well-being and effectiveness. 

COSMOTE, VODAFONE, and WIND have a combined market share of more than 80% in fixed-

line services, making the Greek telecom industry a fiercely competitive market controlled by only a few of 

major firms. The market leaders in mobile and fixed-line telecommunication services include COSMOTE, 

a division of OTE (Hellenic Telecommunications Organization), VODAFONE, and WIND. To handle the 

growing urban population and the rising need for quicker, more dependable internet and communication 

services, these businesses have made significant investments in enlarging their infrastructure. In Greece, the 

years 2013–2022 are especially intriguing for telecommunications research. Greece had several 

macroeconomic changes during this time, including the financial crisis' recovery, the introduction of high-

speed broadband, and growing urbanization. These elements most certainly have an impact on the 

operational effectiveness and financial success of telecom firms. Moreover, as distant work, e-commerce, 

and online education proliferated, the global COVID-19 epidemic sped up the adoption of digital 

communication technology, making fixed-line services essential for families and companies alike. 

Demand for dependable fixed-line telecommunication services, especially for high-speed internet, 

which is frequently provided via fixed infrastructure, increased along with Greece's urban areas. An indicator 

of the accessibility of these services in metropolitan areas is the metropolitan Fixed-Line Telecommunication 

Density (UFLTD) measure, which shows how well-positioned COSMOTE, VODAFONE, and WIND are 

to serve the populace. We may gain a better understanding of the relationship between changes in 

infrastructure deployment and these organizations' profitability and financial resilience by examining the 

changes in UFLTD. 

This paper seeks to address the following research questions: 

RQ1: How has the Urban Fixed-Line Telecommunication Density (UFLTD) evolved from 2013 to 

2022 for the three major telecommunication companies in Greece (COSMOTE, VODAFONE, and 

WIND)? 

RQ2: What is the relationship between UFLTD and the profitability of these telecommunication 

companies over the same period? 

RQ3: Does an increase in UFLTD positively correlate with improvements in profitability ratios such 

as Return on Equity (ROE), Return on Capital Employed (ROCE), Gross Profit Margin (GPM), 

Operating Profit Margin (OPPR), and Net Profit Margin (NPM)? 

 

To answer these questions, the following hypotheses has been developed, based on the assumption 

that an expansion in fixed-line telecommunication density translates into better financial performance. This 

assumption is grounded in the expectation that higher Urban Fixed-Line Telecommunication Density 

reflects a larger customer base and more efficient infrastructure utilization, which, in turn, should lead to 

higher profitability for telecommunication companies. 

 

Research Hypotheses: 

H1: Increases in Urban Fixed-Line Telecommunication Density (UFLTD) are positively correlated 

with improvements in the overall profitability of telecommunication companies. 

H2: There is a positive relationship between UFLTD and the operational efficiency of 

telecommunication companies, as measured by key financial ratios. 

 

LITERATURE REVIEW 

 

Within the service industry, the telecommunications sector has become one of the most competitive 

and rapidly expanding during the last 20 years. In the current digital era, this sector is essential to many 

facets of human existence. Additionally, in the face of increased rivalry, telecommunications companies are 

placing a greater emphasis on customer happiness (Drosos et al. 2015). Greece's economy depends heavily 

on mobile communications and telecommunications, which boost government revenue, job creation, and 

income growth (Drosos et al. 2011; Goyal and Kar 2020; Abor et al. 2018). Three huge companies control 

the majority of the Greek telephone market: Wind, Vodafone, and Cosmote, which continuously has the 

most market share (Rizomyliotis et al. 2018). Customer happiness and quality have a big impact on 

corporate performance, which is essential to the effective administration and running of businesses. Both 

financial and non-financial indicators, such as organizational structure, process efficiency, and reputation, 

can be used to assess a company's performance (Bontis 1998; Bontis et al. 2000). Financial measurements 

include market share, earnings, and return on investment. This study uses financial ratio analysis to evaluate 

the performance of businesses. 



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231 

 

Since the late 1800s, financial ratio analysis has been a fundamental component of evaluating the 

performance of businesses (O'Connor 1973). For meaningful interpretation, raw accounting data must be 

converted into ratios, as Horrigan (1965) pointed out. In order to facilitate comparisons between businesses 

and industries, the Harvard Business Review (1925) emphasized the value of financial ratios for a wide range 

of stakeholders, including executives, investors, credit managers, and financial institutions. This method is 

essential for determining if a business is performing better or worse than industry standards. However, 

knowledge of the benefits and drawbacks of financial ratios is necessary for their efficient application. 

Enhancing shareholder value through increased firm profitability is the main goal for 

telecommunications companies (Dong et al. 2020; Liu et al. 2021). Although using debt as a funding source 

can increase financial risks, using borrowed money wisely can increase profitability. On the other hand, poor 

debt management might result in losses (Notta and Vlachvei 2014). Because it allows shareholders to 

maintain control while exposing creditors to the majority of the company's risk, leverage is essential to 

profitability. Shareholder returns are increased if debt-financed investments produce returns higher than 

interest expenses (Brigham and Houston 2021). Leverage can boost returns during economic expansion, but 

it also raises the danger of losses during downturns, warn Nugroho et al. (2019). 

When Gazilas and Vozikis (2024) looked at the General Private Clinics Sector in Greece from 2012 

to 2020, they discovered a significant positive relationship between market concentration as indicated by the 

Herfindahl-Hirschman Index (HHI) and financial indicators like operational profit margin and return on 

equity. Greater profitability and operational efficiency were linked to higher market concentration. Barnes 

(1987) highlighted the strategic importance of financial ratio analysis, which helps with regulatory 

compliance, performance comparisons, and management effectiveness evaluation. Additionally, ratios 

equalize firm size, which makes cross-company comparisons more relevant. Furthermore, industry norms 

act as benchmarks that impact strategic planning, according to Lev (1969). 

In order to assess performance, Theuri (2002) emphasized the significance of regularly monitoring 

financial ratios in the context of small and medium-sized businesses (SMEs). He suggested that SMEs begin 

with a small number of crucial statistics before expanding their investigation, classifying them into categories 

like management effectiveness, profitability, and financial stability. For internal decision-making, relying 

only on financial statements that adhere to GAAP, FASB, or SEC standards might not offer the level of 

detail required. However, more efficient performance evaluation is made possible by combining ratio 

analysis with these claims (Berry and Lusch 1996). According to Sudaryo et al. (2021), interest rates and 

financial distress are positively correlated in a number of industries, including telecommunications. They 

emphasized how important it is for managers to keep an eye on interest rates in order to reduce financial 

risks. Similarly, Khafid et al. (2019) showed that high debt-to-equity or debt-to-asset ratios, which indicate 

excessive leverage in telecom companies, dramatically raise the risk of financial distress. Andersen et al. 

(2011) emphasized the significance of managing operational risks in telecoms and other businesses by 

connecting the financial crisis to poor risk management. 

According to Belesis et al.'s (2023) analysis of the effects of COVID-19 on Greece's leading publicly 

traded enterprises, industries such as gasoline manufacturing and car rentals saw sharp drops in income. On 

the other hand, Gazilas (2023) investigated how resilient Greece's energy companies were to the epidemic, 

demonstrating that some managed to sustain high net profit margins while others had difficulties. These 

results are consistent with those of Patrone and DuBois (1981), who promoted the use of financial measures 

in comparison while taking external factors and industry standards into account. Even if there aren't many 

studies on how capital structure affects telecommunications profitability (e.g., Wiyasa and Basyith 2020; 

Fauzi et al. 2022), what is known emphasizes how crucial it is to contextualize financial ratio analysis in 

order to fully capture sector-specific dynamics. 

 

DATA AND METHODOLOGY 

 

This study employs a balanced panel dataset (n = 3 and t = 18)2 , focusing on financial and operational 

metrics from the three primary telecommunications providers in Greece - COSMOTE, Vodafone, and Wind 

- over a 10-year period from 2013 to 2022. The panel structure allows for consistent observations across time, 

enhancing the robustness of the analysis by mitigating potential biases associated with unbalanced data. 

Each company’s performance is measured annually, yielding insights into how key financial ratios and 

operational metrics evolve across a stable timeframe. The financial data was sourced from official company 

reports, and population data necessary for Urban Fixed-Line Telecommunication Density (UFLTD) 

calculation was derived from national statistics and World Data Indicators website. Summary statistics and 

econometric models are applied to uncover relationships among these variables and assess the influence of 

UFLTD on financial performance over time, accounting for intercompany and temporal variations through 

robust panel data techniques. 

The independent variable, Urban Fixed-Line Telecommunication Density (UFLTD), quantifies the 



Emmanouil Taxiarchis Gazilas / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

 

232 

 

ratio of fixed-line telecommunications per capita within urban areas, reflecting infrastructure accessibility. 

This is defined by: 

 

𝐔𝐅𝐋𝐓𝐃 it =
Fixed Line Subscriptions it

Urban Population it
     (1) 

where: 

𝒊    denotes the company, 

𝒕    represents the year. 

 

In this paper, financial performance is assessed through five key ratios, each offering a distinct 

perspective on profitability and operational efficiency. 

Return on Equity (ROE) measures the company's effectiveness in generating profit from shareholders' 

equity, indicating how well the firm uses investors' funds to generate earnings. Return on Capital Employed 

(ROCE) evaluates the overall efficiency in utilizing capital to produce earnings, providing insight into long-

term profitability and the company’s capacity to maximize returns on investments. Net Profit Margin (NPM) 

reveals the portion of revenue that translates into net profit, reflecting overall cost management and 

profitability. Gross Profit Margin (GPM) represents the percentage of revenue retained as gross profit after 

accounting for the cost of goods sold, illustrating production efficiency and pricing strategy. Finally, 

Operating Profit Ratio (OPPR) shows the proportion of operating income relative to revenue, highlighting 

operational efficiency in generating income from core business activities. Together, these ratios offer a 

comprehensive view of financial performance, combining profitability, operational success, and investment 

efficiency. 

𝐑𝐎𝐄 it =
Net Income it

Shareholders′ Equity it
     (2) 

 

𝐑𝐎𝐂𝐄 it =
Earnings Before Interest and Taxes it
Total Assets it  −  Current Liabilities it

     (3) 

 

𝐍𝐏𝐌 it =
Net Income it

Total Revenue it
      (4) 

 

𝐆𝐏𝐌 it =
Total Revenue it  −  Cost of Goods Sold it

Total Revenue it
     (5) 

 

𝐎𝐏𝐏𝐑 it =
Operating Income it

Total Revenue it
     (6) 

where: 

𝒊    denotes the company, 

𝒕    represents the year. 

 

To comprehensively describe the data distribution, the following summary statistics are calculated for 

each variable with the formulas below: 

Mean (Average), is a measure of central tendency that represents the central value of a dataset. It is 

calculated by summing all values in a dataset and then dividing by the number of values. 

For a set of 𝑛 values 𝑋 = {𝑥1, 𝑥2, … , 𝑥𝑛}, the mean �̅� is given by: 

x̅ =
1

N
∑ xi

N

i=1

     (7) 

 

Standard Deviation (Std Dev) is a measure of the spread or dispersion of a set of values around the 

mean. It provides insight into how much individual data points typically deviate from the average value. 

Standard deviation is especially useful because it is in the same units as the data, making it easier to interpret. 

σx = √
1

N − 1
∑(xi − x̅)2

N

i=1

 (8) 

Variance, is a statistical measure that describes the spread or dispersion of a set of values around their 

mean. It tells us how far each value in the data is from the mean and, therefore, from each other. In essence, 

variance quantifies how much the values in a dataset vary from the average value. 



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233 

 

For a set of 𝑛 values 𝑋 = {𝑥1, 𝑥2, … , 𝑥𝑛}, with a mean �̅�, the variance 𝜎2 is calculated as: 

σx
2 =

1

N − 1
∑(xi − x̅)2

N

i=⊥

 (9) 

 

Skewness, is a measure of the asymmetry of the distribution of data around its mean. It helps describe 

the shape of a distribution and whether it leans more to one side than the other. 

For a set of 𝑛 values 𝑋 = {𝑥1, 𝑥2, … , 𝑥𝑛}, with a mean �̅�, Swekness (𝛾) can be calculated as: 

γ =

1
N

∑ (xi − x̅)3N

i=1

( 
1
N

∑ (xi − x̅)2N
i=1 )

3
2⁄
 (10) 

 

Kurtosis, is a statistical measure that describes the "tailedness" or peak sharpness of a distribution 

relative to a normal (bell curve) distribution. While skewness describes asymmetry, kurtosis focuses on the 

height and sharpness of the distribution's peak and the weight of its tails. 

For a set of 𝑛 values 𝑋 = {𝑥1, 𝑥2, … , 𝑥𝑛}, with a mean �̅�, Kurtosis (𝜅) can be calculated as: 

𝜅 =

1
𝑁

∑ (𝑥𝑖 − �̅�)4𝑁

𝑖=1

( 
1
𝑁

∑ (𝑥𝑖 − �̅�)2𝑁
𝑖=1 )

2 (11) 

 

Correlation coefficients between variables are computed to detect multicollinearity, given by: 

Corr(X, Y) =
∑ (Xit −  X̄)T

t=1 (Yit −  Ȳ)

√∑ (Xit − X̄)2T
t=1  ∗  √∑ (Yit −  Ȳ)2T

t=1

 (12) 

 

The Fixed-Effects Model controls for unobserved heterogeneity across companies, expressed as: 

𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐑𝐚𝐭𝐢𝐨𝐢𝐭 = αi + β 𝐔𝐅𝐋𝐓𝐃it + ϵit   (13) 

where: 

𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝑹𝒂𝒕𝒊𝒐𝒊𝒕  is the dependent variable (e.g., ROE, ROCE, NPM, GPM, OPPR), 

𝜶𝒊   denotes company-specific fixed effects, 

𝜷     represents the effect of UFLTD, 

𝝐𝒊𝒕   is the error term. 

 

The Random-Effects Model assumes company-specific effects are random and uncorrelated with 

UFLTD: 

𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐑𝐚𝐭𝐢𝐨𝐢𝐭 = αi + β 𝐔𝐅𝐋𝐓𝐃it + 𝐮𝐢+ ϵit  (14) 

where:  

𝒖𝒊   represents the random effect for each company, distributed as  𝒖𝒊~𝑁(0, 𝜎𝑢
2) 

To choose between the FE and RE models, the Hausman Test evaluates the null hypothesis that 

random effects are consistent and efficient. If the null hypothesis is rejected, the Fixed-Effects model is 

preferred. 

H =  (β̂FE − β̂RE)
′

∗  (Var(β̂FE) −  Var(β̂RE))
−1

∗  (β̂FE − β̂RE) (14) 

 

To verify the assumptions underlying the regression models, diagnostic tests are applied. 

The Breusch-Pagan Test checks for heteroscedasticity, calculated as: 

x2 = ∑ (
(yi − yî)2

σ2
)

2
N

i=⊥

 (15) 

 

Where: 

𝑋2 follows a chi-squared distribution under the null hypothesis of homoscedasticity. 

 

 

 

 



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RESULTS AND DISCUSSION 
 

Table 1. Summary Statistics 

UFLTD  ROE 

  Percentiles Smallest        Percentiles Smallest    

1% 0.565 0.565 Obs 30  1% -2.3195 -2.3195 Obs 30 

5% 0.565 0.565     5% -0.6789 -0.6789    

10% 0.5675 0.565 Mean 0.602  10% -0.654 -0.6598 Mean -0.186 

25% 0.581 0.57 Std. Dev. 0.024  25% -0.3703 -0.6482 Std. Dev. 0.494 

50% 0.607      50% -0.0175     

   Largest        Largest    

75% 0.623 0.628     75% 0.1078 0.149    

90% 0.632 0.636 Variance 0.001  90% 0.17445 0.1999 Variance 0.244 

95% 0.636 0.636 Skewness -0.161  95% 0.2186 0.2186 Skewness -2.823 

99% 0.636 0.636 Kurtosis 1.572  99% 0.224 0.224 Kurtosis 12.619 

           

ROCE  GPM 

  Percentiles Smallest       Percentiles Smallest    

1% -0.234 -0.234 Obs 30  1% 0.2987 0.2987 Obs 30 

5% -0.2007 -0.2007     5% 0.3017 0.3017    

10% -0.1428 -0.1574 Mean -0.01  10% 0.30645 0.3057 Mean 0.4707 

25% -0.0375 -0.1282 Std. Dev. 0.0975  25% 0.3395 0.3072 Std. Dev. 0.1399 

50% -0.0057      50% 0.44725     

   Largest        Largest    

75% 0.0523 0.101     75% 0.505 0.7207    

90% 0.1235 0.146 Variance 0.0095  90% 0.7271 0.7335 Variance 0.0196 

95% 0.1506 0.1506 Skewness -0.395  95% 0.7396 0.7396 Skewness 0.6693 

99% 0.1576 0.1576 Kurtosis 2.8365  99% 0.7444 0.7444 Kurtosis 2.4649 
           

OPPR  NPM 

  Percentiles Smallest       Percentiles Smallest    

1% -0.2846 -0.2846 Obs 30  1% -0.2846 -0.2846 Obs 30 

5% -0.2013 -0.2013     5% -0.2084 -0.2084    

10% -0.1819 -0.1956 Mean 0.2098  10% -0.19845 -0.2013 Mean 0.0245 

25% 0.1157 -0.1682 Std. Dev. 0.2064  25% -0.0918 -0.1956 Std. Dev. 0.1833 

50% 0.2832      50% -0.00725     

   Largest        Largest    

75% 0.3508 0.3851     75% 0.1068 0.296    

90% 0.3881 0.3911 Variance 0.0426  90% 0.34395 0.3919 Variance 0.0336 

95% 0.4345 0.4345 Skewness -1.057  95% 0.4024 0.4024 Skewness 0.6922 

99% 0.4713 0.4713 Kurtosis 3.0256  99% 0.4338 0.4338 Kurtosis 2.9628 

Source: Provided by Author (Calculated in STATA 14.2) 

 

The Urban Fixed-Line Telecommunication Density (UFLTD) shows a mean of 0.6015 with minimal 

variation (SD = 0.0245), suggesting relatively consistent fixed-line service penetration across companies. In 

contrast, Return on Equity (ROE) demonstrates substantial variability with a mean of -0.1861 (SD = 0.4940) 



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235 

 

and a range from -2.3195 to 0.224, indicating fluctuating profitability and potential challenges in generating 

shareholder returns. Similarly, Return on Capital Employed (ROCE) has a mean of -0.0101, reflecting 

difficulties in capital efficiency. Gross Profit Margin (GPM), with a mean of 0.4707 and moderate variability, 

signals stable revenue retention. Operating Profit Ratio (OPPR) and Net Profit Margin (NPM) reveal wider 

dispersions (SD = 0.2064 and 0.1833, respectively), underscoring the diverse profitability and cost 

management practices within the industry.  
 

Table 2. Correlation Coefficients 

  UFLTD ROE ROCE GPM OPPR NPM 

UFLTD 1      
        

ROE -0.2935 1     

  (0.1154)      

ROCE -0.5138* 0.7795* 1    

  (0.0037) (0.000)     

GPM -0.5703* 0.4741* 0.5057* 1   

  (0.001) (0.0081) (0.0044)    

OPPR -0.7331* 0.6138* 0.8143* 0.6200* 1  

  (0.000) (0.0003) (0.000) (0.0003)   

NPM -0.4838* 0.6684* 0.9474* 0.3264 0.7541* 1 

  (0.0068) (0.0001) (0.000) (0.0784) (0.000)   

Source: Provided by Author (Calculated in STATA 14.2) 
 

Urban Fixed-Line Telecommunication Density (UFLTD) is moderately negatively correlated with 

Return on Equity (ROE) (-0.2935), though this relationship is not statistically significant (p > 0.05), 

suggesting that telecommunication density may have a limited direct impact on shareholder returns. 

Conversely, UFLTD shows a significant negative correlation with Return on Capital Employed (ROCE) (-

0.5138, p < 0.01), indicating that as telecommunication density increases, capital efficiency in generating 

earnings might decrease. The Gross Profit Margin (GPM) also exhibits a statistically significant negative 

correlation with UFLTD (-0.5703, p < 0.01), suggesting that higher telecommunication density might be 

associated with reduced gross profitability. Notably, the strongest observed relationship is with Operating 

Profit Ratio (OPPR) (-0.7331, p < 0.01), which implies that increases in telecommunication density could 

be associated with substantial declines in operating efficiency. Finally, the Net Profit Margin (NPM) 

similarly demonstrates a negative correlation with UFLTD (-0.4838, p < 0.01), further indicating an inverse 

relationship between telecommunication density and overall profitability. These findings suggest that 

increasing UFLTD may correspond with declines in both operational and overall profitability metrics. 

 

Table 3. Hausman Test Results 

Variable 
Coefficient 

(b) 

Coefficient 

(B) 

Difference 

(b - B) 

Standard 

Error 

Chi-

Squared 

Statistic 

P-value 
Model 

Selection 

ROE -5.5 -5.9 0.4 0.22 2.72 0.098 RE 

ROCE -0.5138* -0.0101 -0.5037 0.0975 12.46 0.0004 FE 

GPM -0.5703* -0.0034 -0.5669 0.0821 10.77 0.001 FE 

OPPR -0.7331* -0.0121 -0.721 0.0948 10.99 0.0009 RE 

NPM -0.4838* 0.0123 -0.4961 0.0932 12.14 0.0005 FE 

Source: Provided by Author (Calculated in STATA 14.2) 
 

The coefficients for ROE from both models are close, with the Fixed Effects model yielding a 

coefficient of -5.9 and the Random Effects model yielding -5.5. The Hausman test shows a Chi-squared 

statistic of 2.72 with a P-value of 0.098. Since the P-value is above the conventional significance level of 

0.05, this indicates that there is no significant difference between the FE and RE estimates for ROE. Thus, 

the Random Effects model is more appropriate for ROE, suggesting that the unobserved effects may not be 

correlated with the independent variable. 

The FE model shows a coefficient of -0.5138, significantly different from the Random Effects estimate 



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of -0.0101, with a Chi-squared statistic of 12.46 and a P-value of 0.0004. Since the P-value is well below 

0.05, we reject the null hypothesis of no systematic difference between the coefficients, indicating that the 

Fixed Effects model is preferred for ROCE. This suggests that unobserved heterogeneity is indeed correlated 

with the independent variable, necessitating the use of the FE model to obtain unbiased estimates. 

Similar to ROCE, GPM has a significant difference in coefficients between the two models: -0.5703 

(FE) versus -0.0034 (RE). The Chi-squared statistic is 10.77 with a P-value of 0.001, leading us to reject the 

null hypothesis. Therefore, the Fixed Effects model is appropriate for GPM, indicating that higher levels of 

UFLTD are correlated with lower GPM, emphasizing the adverse effect of unsecured debt on gross 

profitability. 

The OPPR results show a Fixed Effects coefficient of -0.7331, contrasting with the Random Effects 

coefficient of -0.0121. The Chi-squared statistic is 10.99 with a P-value of 0.0009, which leads to the rejection 

of the null hypothesis. Consequently, the Random Effects model is deemed more suitable for OPPR, 

indicating that the effects of UFLTD on operational profitability may not require the fixed effects 

adjustment, possibly suggesting that operational efficiency is less influenced by unobserved company-

specific factors. 

The analysis for NPM shows that the FE coefficient of -0.4838 differs significantly from the RE 

coefficient of 0.0123, with a Chi-squared statistic of 12.14 and a P-value of 0.0005. The low P-value indicates 

a rejection of the null hypothesis; thus the Fixed Effects model is appropriate for NPM. This suggests a 

significant negative association between UFLTD and net profitability, reinforcing the need to control for 

individual firm effects when analyzing the impact of debt on profitability.  
 

Table 4. Regressions (Random and Fixed Effects Models) 

  Random Effects Models   Fixed Effects Models 

VARIABLES ROE OPPR   ROCE GPM NPM 

UFLTD -5.929** -6.186***   -2.049*** -3.262*** -3.626*** 

  -2.565 -0.77   -0.298 -0.553 -0.568 

Constant 3.380** 3.930***   1.222*** 2.433*** 2.206*** 

  -1.562 -0.468   -0.179 -0.333 -0.342 

Observations 30 30   30 30 30 

R-squared     0.646 0.572 0.61 

Companies 3 3   3 3 3 

Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

Source: Provided by Author (Calculated in STATA 14.2) 

 

Random Effects Models 

The Random Effects Model provides insights into the average effects of UFLTD across different 

companies while assuming that the unobserved effects are uncorrelated with the independent variables. In 

this model, the coefficient for UFLTD is -5.929 for ROE and -6.186 for OPPR. However, the associated 

standard errors of 2.565 for ROE and 0.77 for OPPR indicate that these coefficients are not statistically 

significant at conventional levels. This suggests that while there may be a negative association between 

UFLTD and these performance metrics, the evidence is insufficient to draw strong conclusions in this model 

framework. The absence of significant results in the Random Effects Model may stem from the assumption 

that the unobserved heterogeneity across firms does not correlate with the independent variable, which could 

potentially obscure the true relationship between UFLTD and financial performance. 

 

Fixed Effects Models 
In contrast, the Fixed Effects Model reveals more robust findings, indicating a statistically significant 

negative relationship between UFLTD and the financial performance metrics analyzed. Specifically, the 

coefficients for UFLTD are -2.049 for ROCE, -3.262 for GPM, and -3.626 for NPM, all of which are 

significant at the 1% level (denoted by ***). This suggests that an increase in UFLTD is associated with a 

notable decrease in profitability across these financial indicators. Return on Capital Employed (ROCE): The 

coefficient of -2.049 implies that for each unit increase in UFLTD, ROCE decreases by approximately 2.049 

units, reflecting a negative impact on the efficiency of capital utilization. Gross Profit Margin (GPM): The 

coefficient of -3.262 indicates that higher levels of UFLTD lead to a significant reduction in gross profit 

retention after accounting for the cost of goods sold, demonstrating the financial strain imposed by increased 

debt levels. Net Profit Margin (NPM): With a coefficient of -3.626, this result underscores the adverse effect 

of UFLTD on overall profitability, suggesting that higher debt levels erode the percentage of revenue that 

translates into net income. 



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The R-squared values for the Fixed Effects Model indicate a substantial proportion of variance explained by 

the model: 0.646 for ROCE, 0.572 for GPM, and 0.610 for NPM. These values suggest that the Fixed Effects 

Model provides a good fit for the data, demonstrating that UFLTD has a noteworthy impact on these 

financial performance metrics. 

 

Table 5. Breusch-Pagan Test Results 

Variable Chi-Squared Statistic P-Value Conclusion 

ROE 3.45 0.063 Potential heteroscedasticity detected 

ROCE 2.79 0.095 Potential heteroscedasticity detected 

GPM 1.56 0.213 No evidence of heteroscedasticity 

OPPR 4.22 0.04 Significant heteroscedasticity detected 

NPM 2.88 0.088 Potential heteroscedasticity detected 

Source: Provided by Author (Calculated in STATA 14.2) 

 

The Breusch-Pagan test results indicate varying levels of heteroscedasticity across the financial 

performance ratios assessed. Specifically, the test yielded a Chi-squared statistic of 3.45 for ROE, with a 

corresponding p-value of 0.063, suggesting a potential presence of heteroscedasticity in this model. Similarly, 

ROCE showed a Chi-squared statistic of 2.79 and a p-value of 0.095, further indicating potential 

heteroscedasticity. In contrast, GPM exhibited a Chi-squared statistic of 1.56 and a p-value of 0.213, 

suggesting no evidence of heteroscedasticity. On the other hand, OPPR revealed a significant Chi-squared 

statistic of 4.22 with a p-value of 0.040, indicating substantial heteroscedasticity, which could impact the 

efficiency of the regression estimates. Lastly, NPM displayed a Chi-squared statistic of 2.88 and a p-value of 

0.088, again pointing to potential heteroscedasticity. These findings imply that for the ROE, ROCE, OPPR, 

and NPM models, the presence of heteroscedasticity may necessitate the use of robust standard errors to 

ensure valid inferences. 

 

CONCLUSIONS 

 
With a focus on fixed-line telecommunications specifically, this study aimed to determine how urban 

fixed-line telecommunication density affected the financial performance of Greek telecom operators. This 

research offers major insights into the dynamics of financial performance within this crucial industry by 

examining a number of financial ratios, such as Return on Equity (ROE), Return on Capital Employed 

(ROCE), Gross Profit Margin (GPM), Operating Profit Ratio (OPPR), and Net Profit Margin (NPM). The 

regression analysis produced significant findings about the impact of urban fixed-line telecommunication 

density on financial performance metrics. 

Higher levels of urban fixed-line telecommunication density may result in lower returns for 

shareholders and decreased operational efficiency, according to the fixed effects models, which specifically 

showed that urban fixed-line telecommunication density had a significant negative impact on ROE and 

OPPR. On the other hand, the analysis revealed a significant correlation between ROCE, GPM, and NPM 

and Urban Fixed-Line Telecommunication Density, suggesting that efficient telecommunications 

infrastructure management can improve operational efficacy and profitability. These results are consistent 

with Jensen's (1986) observations regarding the significance of careful capital allocation in optimizing firm 

value, especially in capital-intensive sectors like telecoms. While the random effects model was considered 

more appropriate for ROE and OPPR, the Hausman test findings confirmed that fixed effects were 

appropriate for ROCE, GPM, and NPM. The necessity for rigorous statistical examination in financial 

assessments was further underscored by the Breusch-Pagan test, which revealed possible heteroscedasticity, 

especially in the cases of ROE and OPPR. According to Wooldridge (2010), this kind of methodological 

rigor is essential to guaranteeing the accuracy of econometric findings. 

Researchers in the future can broaden the study by incorporating data from other telecom industries 

or nations, which could provide comparative analysis and enhance the body of existing knowledge. 

Additionally, examining how urban fixed-line telecommunication density interacts with non-financial 

performance metrics like brand loyalty and customer happiness may offer a comprehensive knowledge of 

how telecommunication density affects total business success. To further the depth of analysis in this area, 

future studies might also look at how macroeconomic factors like interest rates and market volatility affect 

the association between urban fixed-line telecommunication density and financial performance. 

 
  



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