A Comprehensive Techno-Economic Model for Fast and Reliable Analysis of the Telecom Operator Potentials

Abstract

Modern telecom operators will have to change their business approach, organization, products development and services development, customer approach and many other important issues if they want to be competitive on the telecom markets in the following years and decades. They will have key roles in this period dubbed the Industry 4.0 era. There are many different models of analysis for telecom operators, but they all have a partial approach to analysis and none of them gives a complete picture of the analysis of telecoms. The Comprehensive Techno-Economic (CTE) model for the analysis of telecom operator potentials is a new and original model for analysis and will significantly help in their transformation processes. This model will enable a quick and easy analysis of the potential of the telecom operator, but also of individual parts of it, regardless of other parts of the company. Despite the fact that the model is easy to define the input data and that it is fast in applying the analysis, it gives precise and mathematically defined results from which one can see the assessment of the potential of telecom or independent assessment of its parts. The main advantages of this model are simplicity, speed of telecom analysis, accuracy of results and its modularity, i.e., independent evaluation of individual parts. Such a model is necessary for telecom operators to achieve fast and reliable potential assessment, analysis, modeling and the easier adaptation of new products and services. This is a unique model with a scientific background and theoretical settings, and it provides practical application in the telecommunication market.

1. Introduction

The era known as Industry 4.0 brings big changes to all business segments and, therefore, people’s lives. Perhaps the biggest impact of this economic revolution will be on the telecommunications segment and thus on telecom operators.

Telecom operators will have a key role in the Industry 4.0 era. Their participation in the full capacity and contribution to the development of the quality of people’s lives will require that they adapt in accordance with the changes brought about by this period. This adjustment will not be easy, and many will make mistakes, further slowing their own development, as well as Industry 4.0 activity. Therefore, it is necessary to have a comprehensive model that will provide an accurate, fast and high-quality assessment of the potential of telecoms, suggesting guidelines for the development and transformation of telecom operators according to the results.

After researching currently known and primarily used models and their applications [1,2,3,4,5,6], it was concluded that there is no model for very fast, high-quality telecom operator potential analysis “from the top to the bottom”.

The analyzed models and frameworks are: Enhanced Telecom Operations Map (eTOM) Frameworks, the Shared Information and Data (SID) Model, the Technology Acceptance Model (TAM), the Training Needs Assessment (TNA), Technological-Organizational-Environmental Frameworks (TOE), Information Technology Infrastructure Library Frameworks (ITIL), Different Techno-Economical Models [7], Different Business Analysis, Different Cost Analysis, the SWOT Model, the PESTLE Model, Porter’s Five Forces, the Ansoff Matrix, the Boston Consultancy Group Matrix (BCG) and other relevant models. All relevant explanations of these models can be found in Appendix A of this paper.

eTOM (Enhanced Telecom Operations Map) Frameworks [8,9,10,11,12,13,14,15,16] defines all details and parts of the telecom service provider activities. The set of documents helps in creating business processes “from end to end” for telecom operators [17]. Sharing information and data model (SID) defines telecom operator business processes and serves the purpose of the quality development of open and automated OSS/BSS systems [18,19,20]. The Technology Acceptance Model (TAM) [21,22,23], together with the Technology-Organization-Environment (TOE) model [24], explore factors of building information modelling (BIM), but the TAM model also analyzes the substantial use of Internet technologies for training and learning purposes. The TOE framework model defines three main levels and their influence on how individual organizations accept innovations based on new technologies and their dimensions and characteristics. The TNA model [25,26,27] creates and defines rules for Human Resources (HR) issues in any company and represents an excellent base for HR in the CTE Model. The IT Infrastructure Library (ITIL) framework [28,29,30] analyzes, among other items in the IT segment, maturity level and smart city readiness. This framework was one of the very good starting points in the development of certain areas (Technological Development Area and Service Development Area) in the CTE model.

Business Analysis models [31,32,33,34,35] analyze different segments in telecom operators; for example, price controls and defining margin rules, competitive strategy, interaction of the OTT business model and the telecom operator, competition and sustainable competitiveness in the business ecosystem, identifying business ecosystems, IT investments in telecom operators and their usability in business processes, and many other purposes. The Cost Analysis Model [36,37,38,39] develops the mechanism of risk-adjusted scheduling and cost budgeting of research and development (R&D) projects in telecommunications, analyzes customer satisfaction, switching intentions, perceived switching costs and perceived alternative attractiveness, and develops case studies for cost allocation regarding flex-grid optical networks and similar projects, churn prediction in the telecommunications sector, etc. TEM serves many different purposes [40,41,42] of analysis. For example, it analyzes pure 5G network models and comparison of the Cognitive Radio and Software Defined Network (SDN) in 5G mobile networks, defines and develops business modeling of optical networks for Metropolitan Area Networks (MAN), evaluates optical disaggregation architectures in the context of metropolitan area networks, and so on.

Also, commonly used models for evaluating certain companies are SWOT, PESTLE, the Ansoff Matrix, Porter’s Five Forces and the BCG Matrix models. All relevant explanations of these models can be found in Appendix A of this paper.

All of the above-mentioned facts point to there being no single model for a quick but precise analysis of the telecom operator’s potential (from top to bottom). In this period and in the years and decades that follow, the telecommunications market will go through many challenges and changes during Industry 4.0. Due to these reasons, it is mandatory to have a unique model for a quick, precise, and high-quality assessment of the potential of telecom operators with an assessment of the main advantages and disadvantages. The CTE model was created exactly for this purpose.

During the Industry 4.0 era, the telecommunications market will change drastically. This is being reflected in almost all business segments, from the introduction of new technologies, through the development of new products, services and the development of sales networks and customer service, as well as a greater orientation towards the development of its own employees. Because of such rapid, sweeping changes, there is a need for such a model and a completely different approach for analysis than is prevalent in current models used for analyses in telecommunications.

This article presents and describes the Comprehensive Techno-Economic (CTE) model for the assessing of telecom operator potentials. The CTE model is a new and original model based on the research of existing models of analysis and experience in telecommunications. This model is fast, reliable and efficient in assessing the potential of a particular telecom and provides accurate potential assessment results. In addition, this model is modular and can be used to estimate individual parts of telecoms. The CTE model can also be used to compare two or more telecoms from the same or different countries.

The main goals of the CTE model are defined as follows:

• To calculate and evaluate the potential of a telecom operator easily and quickly with remarkably high accuracy;
• To calculate and evaluate the potential of area(s) of the telecom operator easily and quickly with very high accuracy;
• To compare two or more telecom operators from one or more countries;
• To aid managers and employees in making certain strategic business decisions.

This model can be used wholly unto itself, but can also be used modularly: one or several items from one or more areas, an entire area or several areas together for assessing the data of telecom operators(s). It measures the potential of internal items on the business of telecoms but also assesses the potential of telecom resilience in the face of external factors. The model gives results that will help to support relevant business and strategic decisions, which have been noticed as the main shortcoming of the existing models used for telecom modeling and adaptation to the changes brought forth by Industry 4.0.

In this research, of which one of its parts is shown in this paper, two hypotheses have been proven and thus confirmed:

Main hypothesis: By developing a modular model for telecom operator analysis, and based on defined areas and defined items for analysis within certain areas, it is possible to make an objective quantitative assessment of individual segments of telecom operators for better and more efficient operations.

Second hypothesis: This model will enable comprehensive or partial qualitative and quantitative comparison among telecom operators in the same or different countries.

The scientific contributions of this research and final model are:

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The development of a new modular model of the telecom operator, which will make it possible to objectively and more easily optimize the adoption of certain key and strategic technological and business decisions.

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The application of the model in optimizing different areas of a telecom operator separately and independently, easily and quickly.

In Section 2, a brief presentation of the scientific literature is given in order to show that significant research is being carried out regarding the transformation of the telecommunications market in the Industry 4.0 era. Changes in the telecommunications market directly affect the need for fast and high-quality transformation of telecom operators in order to respond to these changes.

In Section 3 of this paper, explanations are given for each of the areas (

Table 1. The distribution of fields in the CTE model.

	CTE Model
Technical Level	Coverage and Accessibility to Users		Technological Dev. and IT Development
Business Level	Products Development	Services Development	Sales and Customer care	Human Resources (HR)
Environmental Level	Political, Financial, Regulatory and Law environment		Quality of Brand and Presence in public

) and a description of one item with the corresponding equation. Other items with associated equations are defined and described in the Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H, Appendix I, Appendix G, Appendix K, Appendix L, Appendix M, Appendix N, Appendix O, Appendix P.

Table 2. CTE model—brief explanations and connections to the text in the article.

	CTE Model Chapter: Section 3; Appendix B
TL Section 3.3. Appendix C	Coverage and Accessibility to Users The quality of accessibility to users via fixed and mobile technology. To: Technological and It Development Area, Products Development Area, Services Development Area, Sales and Customer Care Area, Quality of Brand and Presence in public. From: Sales and Customer Care Area Chapter in the article: Section 3.3.1. Appendix in the Article: Appendix D.		Technological Dev. and IT Development The quality of the implementation of modern telecommunications technologies, but also with regard to the readiness to accept new advanced systems for new advanced services of the future. To: Products Development Area, Services Development Area From: Sales and Customer Care Area Chapter in the article: Section 3.3.2. Appendix in the Article: Appendix E.
BLSection 3.4.	Products Development The quality of products in fixed and mobile parts. Sales and Customer Care Area, Services Development Area, Quality of Brand and Presence in Public Area Coverage and Accessibility to Users Area, Technological and IT Development Area Chapter in the article: Section 3.4.1. Appendix F.	Services Dev. The quality of new services implementation in the telecom system. Sales and Customer Care Area, Quality of Berend and Presence in Public Area Q and A Area, Tech. and IT Dev, Products Development Area Chapter in the article: Section 3.4.1. Appendix G.	Sales and Customer care The quality of sales and customers care approach to users. Forward links: None. C and A to Users Area, Technological and IT Dev. Area, Products Dev Area, Services Dev. Area. Quality of Brand and Pres. in Pub. Chapter in the article: Section 3.4.2. Appendix H	Human Resources (HR) The quality of all important aspects in HR area—from managers to employees, investment in education and trainings … Influence (not direct links) to all Areas in TL and BL levels + Quality of Brand and Presence in Public Influence from all Areas from TL and BL levels + “P, F, RandL Env” Chapter: Section 3.4.3. Appendix I
ELSection 3.5.	Political, Fin., Regulatory and Law environment The quality of resistance that telecom has to external influences and the potential of using the environment Influence to All other Areas None Chapter in the article: Section 3.5.1. Appendix J		Quality of Brand and Presence in public The quality of telecom influence to users and potential users. Forward links: Sales and Customer Care Area Cand A to Users area, Products Dev Area, Services Dev Area, Chapter in the article: Section 3.5.2. Appendix K

lists the basic objectives of the analysis of each of the areas, the impact of forward-backward connections between the areas and links to parts of the text and appendices for all areas.

Section 4 describes the use of the model for the comparison of three telecoms in the same country and the method of modular application of the model. In that chapter, the method of reading the results was also shown, and it was shown how to use the model to obtain guidelines for certain strategic business decisions.

Section 5 shows the use of the entire model for the analysis of one telecom, as well as a presentation of the results and how to use them for the purpose of obtaining guidelines for making strategic business decisions. At the end of the paper, a detailed conclusion is given (Section 5) about the model, its advantages, how to use it and the continuation of research to improve the model. The paper includes 16 Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H, Appendix I, Appendix G, Appendix K, Appendix L, Appendix M, Appendix N, Appendix O, Appendix P that serve to facilitate the understanding of the use of the model in practice.

2. A Review of the Relevant Literature for the Analysis of Telecom Operators

There is much research circulating around the world dealing with the changes and adaptations of telecoms in the Industry 4.0 era, which will have a significant impact on their business in the coming years and decades. For example, [43] provides empirical evidence for sustainable growth research and useful insights for practitioners to maintain sustainable growth for major telecom opaerators in China. Based on the resource-based view (RBV), this study explores the factors that influence sustainable growth. In [44], systematic research was carried out on collaboration and adaptation attempts between business models and technological innovation. Research shows that the novel mode is beneficial both to telecom users and telecom enterprises. Corporation social responsibility (CSR) is a key topic in the next analyzed article [45]. This paper aims to evaluate and analyze the maturity of CSR practices through an empirical study for telecom companies in KSA. Article [46] analyzes relationships between telecom operators and OTT service providers. The booming OTT business has had a significant impact on traditional telecommunication businesses, such as with voice and short message services, and the sense of crisis among telecommunication operators shows that the channeling trend has become increasingly obvious. Faced with competition on the distribution of interests between channels and content, telecommunication operators and OTT service providers are engaged in fierce competition and cooperation simultaneously.

The study [47] investigates the factors affecting Bharti Airtel’s cross-border postacquisition performance in an African market. This article analyzes and describes the relationships among numerous factors such as technical capability, affiliated firms’ absorptive capacity and organizational learning capabilities. Their relationships determine successful operations of the analyzed acquisition deal. The analysis and study of three telecoms is presented in article [48]. This article gives managers and stakeholders (including customers, capital owners and employees) a means to understand major changes and determinants of value creation and distribution. The main focus here is on explaining the extent to which various stakeholders—employees, customers, capital owners and government—were able to appropriate the value created by the firms.

Article [49] deals with the issue and comparison of three telecoms in Yemen. This survey reveals that a significant link with technology acceptance and use exists between device automaticity, user experience, system efficiency and information quality. According to the study findings, the adoption of the proposed model will play an important part in the successful application of the modern technology in Yemeni companies. The next paper, [50], deals with the examination of knowledge management and market orientation, innovation and organizational performance. This research was conducted on the telecommunication market in Pakistan. The objective of this research was to examine the impact of knowledge management orientation on a firm’s performance with the mediating role of organizational innovation and market orientation. The presented results of the study demonstrate that knowledge management orientation plays an affirmative role in the promotion of organizational performance. Another paper deals with the link between technology, knowledge management and service development in the telecom industry in Indonesia during Industry 4.0 [51]. This further confirms the interest of scientists and experts in research in the field of telecommunications during the Industry 4.0 era. The authenticity of this research lies in the description of how management emerges with a practical oriented framework of how organizations must be formed to be innovative and competitive through the general arrangement of antecedents of service innovation. Research conducted in Poland is presented in the following paper [52]. The aim of the research is to discover new knowledge allowing for the description and design of 4.0 business models using network effects. The results of the study present the possibility of using the network effect in business models 4.0. The paper develops a framework for business model analysis from the perspective of Industry 4.0. The presented research will allow for an indication of the possibility of using a business model from the perspective of Industry 4.0, based on the theory of the network effect in developing the value of network organizations. In recent years, the influence of Industry 4.0 on the telecommunications market has been researched and analyzed extensively. Thus, [53] analyzes a new approach in the creation and development of new advanced telecom services. New approaches are now on the production floor—flexible but ultra-reliable, low latency wireless communications through interoperable systems that share data. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. Within the framework of this research, many articles and studies conducted around the world have been investigated; among them, only one more—[54]—will be presented here. The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.

Research and development of the CTE Model was started in 2015. From then until today, this model has undergone certain changes and additions. Other research the world over has been followed along with in order to confirm the need for such a model. In this overview, and in the previous part of the text, only research from the past few years that has been carried out in many countries around the world (China, Indonesia, Poland, South Africa, Yemen) are listed. The amount of research in this area is growing significantly, for which the thesis that this area requires further elaboration is confirmed. This is precisely why there is a recognized need for a model that can, relatively quickly and without the engagement of many people, and, at the same time accurately and qualitatively assess the potential of telecoms in the output of the Industry 4.0 era, suggest what needs to be changed and/or further improved. One such model is missing in this field because all the others analyzed and presented in the Introduction and Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H, Appendix I, Appendix G, Appendix K, Appendix L, Appendix M, Appendix N, Appendix O, Appendix P are too complex and require much time and human potential to use them. This research was started for that reason, and the results will be presented in a subsequent part of the text.

3. Description of Comprehensive Techno-Economic (CTE) Model

3.1. Disadvantages and Shortcomings of Existing Models for the Analysis of Telecom Operators

All previously mentioned models work with a large dose of subjectivism, and yet no model offers a complete picture and overview of the state and potential of telecom operators. This research, which was conducted over the last few years, has defined the need for creating a unique and comprehensive model that could minimize and even eliminate subjectivity in the results.

3.2. Description of Levels, Areas, Segments and Items in the CTE Model

The first and basic division in the model is the division into levels. This model has been defined by three levels:

• Technical Level (TL)
• Business Level (BL)
• Environmental Level (EL)

These three levels will consist of different segments, which are the second division of the model. Individual segments will be grouped into areas because all segments do not have the same importance in terms of the analysis of telecom potential [54,55].

The final number of different defined segments in this model is fourteen. These fourteen segments can describe any telecom operator potential from the top to the bottom. They are as follows:

• Coverage and accessibility to users (TL),
• Technological development (TL)
• IT Development (TL)
• Products Development (BL)
• Services Development (BL)
• Sales (BL)
• Customer Care (BL)
• Human Resources—HR (BL)
• Political Environment (EL)
• Regulatory Environment (EL)
• Law Environment (EL)
• Finance Environment (EL)
• Quality of Brand (EL)
• Presence in public (EL)

The last divisions in this model are items. Each segment/area is defined by specially defined items that essentially describe and give value to the entire area. Each of the items is mathematically described, their sum revealing the total value of the area. The actual version of the CTE model has three levels, eight areas, fourteen segments and various items in each area. The format of the model is “2–4–2”. All items in each area have a maximum worth of 0.1 (10%), and each area has a maximum value equal to 1 (100%). Table 1 shows and explains this distribution.

The results will be presented as follows:

• Total amount of CTE Model for individual telecom operator potential;
• Total Area Value (max. value of each area is 1);
• Each item’s value in each area;
• Tabular and graphical forms for easier comparison of telecoms.

Before a more detailed description of individual areas and items, a tabular representation of the relationship between areas, their mutual influence and connections to individual parts can be found in the article. The table will explain the following:

• Main targets: Assessment and calculation of telecom potential regarding the specific areas;
• Forward links: Showing the influence of the observed area on other areas of the model;
• Backward links: Showing which other areas have an influence on the observed area;
• Chapter in the article: Link to chapter in article;
• Appendix: Link to Appendix in article.

Table 2 gives a brief overview and main goals of the analysis in individual fields, mutual links between fields and links to chapters and appendices in the text. In this way, it is easier to understand the principle and mode of operation of the model and to obtain additional information about individual areas and items within them.

3.3. Technical Level in the CTE Model

The Technical Level consists of three segments, which are as follows:

• Coverage and accessibility to users (TL);
• Technological development (TL);
• IT Development (TL).

According to the characteristics of these segments and what they provide in their outputs to assess potential of the telecom, these segments provide two areas.

3.3.1. “Coverage and Accessibility to Users” Area

The following ten (10) items have been defined in the C&A area:

• Mobile connectivity and availability:
  • Quality of mobile data access in urban areas (outdoors);
  • Quality of mobile data access in special parts of urban areas—areas of mass gatherings;
  • Quality of mobile data access in rural areas;
  • Quality of mobile data access on roads: highways and main state roads;
  • Quality of mobile data access on roads: regional roads and local roads.
• Fixed connectivity and availability:
  • Distribution and number of transmission fiber optics cables (fibers) at the state level among urban settlements;
  • Percentage of connections to homes (houses, flats, apartments…) by fiber optic cables—FTTH (fiber to the home);
  • Percentage of connections to factories, business facilities, incubators, etc. by fiber optic cables—FTTBus;
  • Local loop shortening of the copper network—percentage number of households and companies that are less than 500 m from the last telecom connection point;
  • Quality of protection of the primary transmission system.

According to all surveys and research, the future of telecommunications is based on the (mobile) Internet and services based on connection to the (mobile) Internet. Some services will be sensitive and dependent mostly on download speed, some on upload speed, some will be sensitive to latency and others to different combinations of these two or even all three factors. In order to analyze the quality of the mobile signal (first five items), the following items will be measured for a mobile sub-segment in the “C&A area”:

• Download data rate (Mb/s);
• Upload data rate (Mb/s);
• Delay of data signal (ms).

The mathematical equations for calculating of the first five items is given as follows:

$$QoMD=\left(\frac{AvgADD·F_{DL}}{RefADD}+\frac{AvgADU·F_{UL}}{RefADU}+\frac{RefDY·F_{DY}}{AvgDY}\right)·0.1$$

where:

QoMD = The Quality of Mobile Data access

AvgADD represents the Average Access to Mobile Data Download, calculated as:

$$AvgADD=\frac{1}{N}{\displaystyle \sum }_{i=1}^{N}AD{D}_i$$

RefADD represents the Referent Download Access Speed (this value changes according to the development of mobile systems),

AvgADU represents the Average Access to Mobile Data Upload, calculated as:

$$AvgADU=\frac{1}{N}{\displaystyle \sum }_{i=1}^{N}AD{U}_i$$

RefADU represents the Referent Upload Access Speed (this value changes according to the development of mobile systems),

RefDEL represents the Referent delay of sampling (this value changes according to the development of mobile systems),

AvgDEL represents the Average delay, calculated as:

$$AvgDEL=\frac{1}{N}{\displaystyle \sum }_{i=1}^{N}DE{L}_i$$

F_DL represents the Factor that defines download importance,

F_UL represents the Factor that defines upload importance,

F_DY represents the Factor that defines delay importance,

N represents the Number of samples,

Number 0.1 represents the maximum worth of this item.

It is important to note that:

F_DL + F_UL + F_DY = 1

It is important to define where, when and how many times mobile signal samples will be taken considering the population and surface of an urban area, locations of rural areas, highways, main state roads, regional roads and local roads [56,57,58,59,60,61,62,63]. These questions—and answers to them—make for differences among the five items. Precise descriptions and definitions of the sampling method for all the first five items of this area are an integral part of the model. The sum of these items gives us the total value of the potential of the telecom operator in terms of accessibility to users via mobile networks.

Another five fixed items, which are also mathematically defined and precisely described, will not be explained here due to a lack of space. It is important to state that the calculation of this area gives us the qualitative and quantitative value of a telecom, i.e., its potential in terms of accessibility to users.

3.3.2. “IT and Technological Development” Area

Another area at the Technical Level in the CTE model is the “Technological and IT Development area“. It consists of two segments: “IT Development“ and “Technological Development“. This area has ten items which are related to technological and IT issues. All items in this area are calculated as QoIT&T (Quality of IT&T item). The items may be changed or supplemented over the coming years and decades in accordance with changes and developments in the telecommunications and IT markets, as well as customer needs for various services.

The result of the mathematical equation for one of the items from the area is presented here. The billing system is one of the most important systems for a telecom operator. Billing systems face several important issues, one of which is the implementation of new changes regarding existing tariff models, tariff options, tariff groups and groups of tariffs. This last issue is crucial for assessing the potential of the telecom—the speed of response and action in terms of creating new and/or supplementing existing tariffs, groups, options and more.

The equation describing this item is as follows:

$$Qo{S}_{BS}=\left(\frac{Ref{T}_{TM}·F_{TM}}{Max{T}_{TM}}+\frac{Ref{T}_{GTM}·F_{GTM}}{Max{T}_{GTM}}+\frac{Ref{T}_{ET}·F_{ET}}{Max{T}_{ET}}\right)·0.1$$

The CTE Model gives definitions for calculating RefT values and factors F values. Factors F values define the importance of all parts from the equations.

3.4. Business Level in the CTE Model

The second level in this model is the Business Level (BL). There are four areas: the “Products Development Area”, the “Services Development Area”, the “Sales and Customer Care Area” and the “Human Resources Area”.

3.4.1. Two Business Level Areas in CTE Model: Products Development and Services Development

The Products Development Area is best described as “creating and developing different types of tariff models, tariff options, groups of tariffs and/or tariff groups for different types of private, business, and/or public service customers”. The Services Development Area is best described as “creating and developing different types of service models for satisfying and meeting different customer needs for communication, entertainment, business or any other purpose”.

Products development items represent the quality of different tariff packages, tariff options, groups of tariffs and tariff groups. The emphasis will be on the access to the mobile Internet and on the offer of various new services. For this reason, these items will have to be analyzed from time to time, continuously monitored and changed according to the development of this market in the future. This article gives us an example of the equation “Pre-paid mobile tariff packages“, which is described by the following equation:

$$QoT{M}_{PrP}=\left(\frac{Qo{D}_{MO}·F_{Data}}{Qo{D}_{Ref}}+\frac{Qo{V}_{MO}·F_{Voice}}{Qo{V}_{Ref}}+\frac{Qo{S}_{MO}·F_{SMS}}{Qo{S}_{Ref}}\right)·0.1$$

This equation consists of three parts, which explain the main segments for pre-paid customers: voice (minutes), SMSs and data traffic included in tariff model(s). The importance of these segments is defined by different factors F. The CTE model defines all important issues regarding referent values (Ref) and factor F values. This item will give results for pre-paid product potential in the analyzed telecom operator, but also indicates the possibility of, and gives guidelines for, improving the offer [64,65,66].

Another area on this level is the Services Development Area. Almost all items in this area will be based on new technologies, and this is the most important direction for all modern telecom operators. The items in this area will give us assessments of quality and mass of new services, as well as diversity of services in terms of different business areas (industry, medicine, cities, agriculture, etc.) which the telecom operators offer [67,68,69]. Appendix G gives us all the important explanations and equations of these items. In this article, we show one example of the item equation “IoT specialized services”.

The equation for “IoT specialized services” is as follows:

$$QoIo{T}_{SS}=\left(\frac{QoIo{T}_{SS1}·F_{SS1}}{QoIo{T}_{RefSS1}}+\frac{QoIo{T}_{SS2}·F_{SS2}}{QoIo{T}_{RefSS2}}+\frac{QoIo{T}_{SS3}·F_{SS3}}{QoIo{T}_{RefSS3}}+\frac{QoIo{T}_{SS4}·F_{SS4}}{QoIo{T}_{RefSS4}}+\frac{QoIo{T}_{SS5}·F_{SS5}}{QoIo{T}_{RefSS5}}\right)·0.1$$

This equation shows that the top five “specialized IoT services” will be analyzed. After a detailed analysis and research, the actual top five specialized services are smart city services, smart home services, smart medicine services, smart agriculture services and smart energy services. These top 5 specialized services might be changed in the following months or years, and will be necessary to analyze and change this ranking. Factors F define the numerical value (percentage) of each of the specialized services. The CTE model defines all necessary information and inputs how to calculate QoIoT_SSN and QoIoT_RefSSN (N = 1 to 5).

3.4.2. “Sales and Customer Care” Area

This area and related items will analyze existing standard sales and customer care channels, as well as new sales and customer access channels. An example of an item regarding the “Quality of the distribution of outlets in the country” is presented by the following equation:

$$QoSP=\left(\frac{QoS{P}_{UA1}·F_{UA1}}{QoS{P}_{RefUA1}}+\frac{QoS{P}_{UA2}·F_{UA2}}{QoS{P}_{RefUA2}}+\frac{QoS{P}_{UA3}·F_{UA3}}{QoS{P}_{RefUA3}}+\frac{QoS{P}_{UA4}·F_{UA4}}{QoS{P}_{RefUA4}}+\frac{QoS{P}_{UAA}·F_{UAA}}{QoS{P}_{RefUAA}}\right)·0.1$$

This equation is divided into five parts. These parts analyze different urban areas (UA1–UA4 and UAA). The CTE model defines all details and definitions related to the measurement and calculation of reference values and factors in this item. The value of this item gives us the value of the total quality of the distribution of points of sale. Also, its parts give us the quality of the distribution of points of sale according to individual urban areas.

3.4.3. “Human Resources (HR)” Area

Human Resources (HR) is a part of all companies in all business sectors. Human Resource Management (HRM) has many definitions [70,71,72]. HRM focuses on managing people at work or employees who make up an organization. Each company must establish goals for HRM. The main goals are as follows [73]: increasing the net assets of owners, enhancing employee development and satisfaction, maximizing customer satisfaction, achieving cooperate growth, increasing market share, fulfilling social responsibility, achieving financial stability and increasing the quality of products and services. According to another source [74], managerial functions of personnel management involve planning, organizing, directing and controlling (

Table 3. The functions of HRM.

Managerial Functions	Operative Functions
Planning	Employment
Organizing	HR Development
Directing	Compensation
Controlling	Human Relations
	Industrial Relations
	Recent Trends in HRM

).

The CTE model gives us definitions and equations for all items in this area. This article gives us an example of the item “Quality of managers”. It is defined as:

$$QoM=\left(\frac{Qo{M}_{TL}·F_{TL}}{Qo{M}_{RefTL}}+\frac{Qo{M}_{ML}·F_{ML}}{Qo{M}_{RefML}}+\frac{Qo{M}_{LL}·F_{LL}}{Qo{M}_{RefLL}}\right)·0.1$$

The equation consists of three parts and defines the assessment of three levels of management: top, medium and low. The equation can have more components, but through the performed analysis, this distribution provides the optimal approach.

3.5. Environmental Level (EL)

The Environmental Level (EL) consists of six segments joined into two areas. The first area has an impact on telecoms, while the second area represents the impact of telecoms on the environment. This level completes the picture of the potential of a telecom with respect to interaction with the environment.

3.5.1. “Political, Financial, Law and Regulatory Issues” Area

The “Political, Financial, Law and Regulatory issues” area consists of four different segments. These segments have very strong relationships and mutual influences. This area defines the impact of these key external factors on the telecom business. The outputs of the area will show the resistance of telecom to the effects of its environment and its potential on the market. The CTE model defines all items in this area. The item equation “The potential of international business visits to the state” is as follows:

$$PoBV=\left(\frac{UoB{V}_{UTD}·F_{UTD}}{NoB{V}_{UTD}}+\frac{UoB{V}_{MTD}·F_{MTD}}{NoB{V}_{MTD}}\right)·0.1$$

This equation analyzes business visitors and their activities in the telecom operator network. It has two separate parts: it analyzes the business visitor for “up to three days” (UTD) and “more than three days” (MTD). After conducting analyses, it was concluded that most business conferences and similar events last up to three days. Visitors to such events are generally larger and better consumers of telecom operators than business users who come to visit for more than three days. Factors F give us a description of the values for both items.

3.5.2. “Quality of Brand and Presence in the Publicity” Area

This area consists of two segments: “Quality of Brand” and “Presence in publicity”. Brand quality is defined as the recognition of the entire company quality (products, services, customer approaches, etc.), which has an influence on consumer purchasing behavior. Public presence is defined as the reach that a telecom has within the public and how publicity recognizes its activities. These two segments are well intertwined and are therefore placed in a common area.

Digital advertising has become crucial for the promotion of products and services, but also for building a telecom operator brand. The social network LinkedIn is the most important business network in the world. It has been set as one of the key items for presenting telecom operators to the public, with an emphasis on the business segment. The item “Quality of digital advertising—usage of LinkedIn” is presented by:

$$QoAd{v}_{Ld}=\left(\frac{NoF{W}_{Ld}·F_{FW}}{NoF{W}_{RefLd}}+\frac{NoA{C}_{Ld}·F_{AC}}{NoA{C}_{RefLd}}+\frac{NoC{O}_{Ld}·F_{CO}}{NoC{O}_{RefLd}}\right)·0.1$$

This equation has three different parts. It analyzes the number of followers, activities, positive comments and replies to comments by telecom operator administrators. The importance of these three parts is defined by different factors F.

4. Discussions and Verification of the CTE Model on the Example of the “Coverage and Accessibility to Users” Area

4.1. Discussion of the Model

In this part of the paper, the focus will be on the C&A area (TL), or, more precisely, on the first two items in the area. The goal is to prove the modularity of the model, i.e., to show how it is possible to use individual areas or only individual items independently of the overall model. The first two items in the mobile part of the C&A area are:

• Quality of access to mobile data in urban areas (outdoors)—MDUA;
• Quality of access to mobile data in special parts of urban areas—areas of mass gathering—MDMG.

A city of approximately 105,000 inhabitants will be taken as an example (the inner-city area has about 60,000 inhabitants) in a country with approximately 3.5 million inhabitants. According to this calculation, this city belongs to Urban Area 1. The inner-city area covers about 16 square kilometers. The observed telecoms, whose potential are in focus, operate in combination on 3G/3.75G/4G/4G+ mobile network standards. In the observed city, mostly 4G+ (LTE Advanced) mobile base stations have been installed. This is an important fact [75,76,77,78] for more easily and precisely defining the manner, number and location of mobile signal sampling.

For defining places, the method of taking samples, time of taking samples and the number of samples, it is necessary to consider these important facts: definition of the urban zone, number of inhabitants of the inhabited place, square footage of the inhabited place, generation of the mobile network and special rules defined for taking samples in the open space (outdoor) and in places involving the mass gathering of people [79,80,81,82].

A complete and comprehensive discussion, clarifications, necessary descriptions and critical analysis with a list of some of the literature used is provided in Appendix L.

4.2. Signal Meassurement Results for the Three Independent Mobile Telecom Operators

The results of measurements of download data rate, upload data rate and signal latency for three independent mobile telecom operators will be shown. Measurements will be carried out outdoors in the city center (approximately 1 km²) and within the largest shopping center in the city, which has three underground floors, a ground floor and four above-ground floors. Reference values for the potential evaluation for a first (universal) approach have already been defined in Appendix L. Reference values for a direct comparison (second approach) of three mobile operators will be the highest values of all samples for downloading and uploading, as well as the lowest value of all samples for the delay of signals.

Table 4. Reference values for the MDUA item (Ref).

	Comparison of Mobile Telecom Operators—I	Comparison of Mobile Telecom Operators—II
RefADD	300 Mb/s	134,22 Mb/s
RefADU	150 Mb/s	42.41 Mb/s
RefDEL	10 ms	40 ms

and

Table 5. Reference values for the MDMG item (Ref).

	Comparison of Mobile Telecom Operators—I	Comparison of Mobile Telecom Operators—II
RefADD	300 Mb/s	68.59 Mb/s
RefADU	150 Mb/s	42.45 Mb/s
RefDEL	10 ms	49 ms

show these values.

For the purposes of this paper, a short survey has been conducted among users. The question was asked: What do you need more for your business? The answers offered were DL speed and UL speed, both equally in terms of latency. The obtained results are shown in the

Table 6. Results of the surveys.

Items	Results of Survey
DL speed	20%
UL speed	15%
Both equal	65%
Latency	0%

and will be used to define the F factor in the calculations of telecom potential for smart city services and to compare telecoms.

Factors F for the direct comparison of mobile telecoms for customers will be F_DL = 20/(20 + 15) = 0.57, F_UL = 15/(20 + 15) = 0.43 and F_DEL = 0. These factors will be used for calculations of MDUA and MDMG items for telecom potential comparison.

Measured outdoor signal samples (used for calculating the MDUA item) for all three mobile operators show significant instability, i.e., signal, especially in DL and UL items, varying significantly. Signal latency samples for all three mobile operators are stable, and we note that the Second Mobile Operator (SMO) shows the best characteristics and has the lowest average value. Although the First Mobile Operator (FMO) has the best results for DL and UL values, this operator must change approach in coverage due to the huge deviations of signal samples. This statement is especially noted for UL signal patterns. FMO needs to increase the signal gain through a certain redirection of antennas and build several new base station locations for better coverage among high buildings near city centers. Of course, more detailed analyses are needed for more precise guidelines. The SMO and the TMO have to build several new base station locations for gaining a better signal, increasing the access transmission speed towards almost all the base stations and in parallel through a certain redirection of antennas, to increase the signal gain. Appendix M shows all graphs for these statements and better understanding regarding the presented results.

MDMG measured samples show several interesting conclusions. The measured samples were obtained by measuring in the largest sales center. On the underground floors, SMO and TMO had no signal and it was not possible to take samples while the samples for FMO had significantly high values and were stable. It is obvious that FMO has covered this area with several micro/pico base stations. SMO and TMO will have to cover these underground floors, as this represents a significant drawback for them. Average values for these operators were derived without these samples, but this was noted in the final analysis as a significant shortcoming.

The ground floor and four floors above ground are covered by the signal of all three operators. The FMO shows the best results, but it can be noticed that the signal samples of all three operators are quite unstable. It indicates the fact that the coverage is made through external base stations and that there are no micro and pico base stations inside the mall (or, alternatively, there is a negligible number).

Finally, MDUA and MDMG values will be calculated for both approaches. The final values will be shown in

Table 7. Calculated items for the first approach.

	FMO	SMO	TMO
MDUA	0.0164	0.01323	0.01376
MDMG	0.017	0.00973	0.00761
Total	0.0334	0.02296	0.01986

and

Table 8. Calculated items for the second approach.

	FMO	SMO	TMO
MDUA	0.04711	0.02064	0.02398
MDMG	0.05686	0.01245	0.01646
Total	0.104	0.03309	0.04044

.

The measured and obtained results clearly indicate the fact that none of the observed three mobile operators is ready to provide and support more demanding IoT services. Parallel signal measurement was performed to the server (for FMO) within the city (average delay is 18.28 ms) and the result of MDUA + MDMG increases to 0.069. The maximum amount of each item is 0.1 (the sum of these items is 0.2), and it is clear that the FMO has some potential to provide a certain level of IoT services. The recommendation for all three operators is that it is necessary to significantly improve the quality of the network in the city, especially since the observed city is located in Urban Zone 1.

The measurement results show that the FMO has the best results. It should be noted that the FMO has a significant place for progress and that the coverage of both open and closed space needs to be improved through the addition of new locations in the city, but also through the determination of antenna redirection. TMO showed better results than SMO, but this difference is not significant. Both operators must greatly improve the quality of coverage in the outdoors, but especially indoors, because it is not acceptable that both operators do not have a signal in the underground area of the largest shopping center in the city. The analyses presented in this chapter show how the CTE model can be used modularly (by items and areas) and for the purposes of the comparison of mobile operators. This analysis confirms the robustness and modularity of the CTE model, as well as its effectiveness in order to obtain concrete proposals for the improvement of certain areas of operation of the operator.

5. Discussion and Verification of the Entire CTE Model

In this part of the paper, the use of the CTE model for estimating the potential of one telecom operator will be presented. The First Telecom Operator (FMO) will be taken as an example. Some of the data used in the analysis will be accurate and taken by measurement or otherwise (data from the official website), and yet some will be taken with certain approximations and assumptions because accurate data could not be obtained. However, ultimately, it is important to show how the CTE model works in practice.

5.1. Discussion of the Model

This chapter will show how to use the CTE model. There will be no going into the depth of the method of data collection, as well as a deeper analysis of the results, but a presentation of the obtained values will be given, and the overall result will be commented upon. Each result from the eight areas will be briefly explained (Appendix N) with regard to the observed telecom operator. Finally, the overall result will be presented and its significance will be commented upon. The CTE model serves for a quick qualitative and quantitative assessment of the potential of a particular telecom and as an aid in its making certain business and strategic decisions. The aim is to show and prove the hypotheses presented in this paper and to show how one model can help in making key strategic business decisions. For more details, please see Appendix N.

5.2. Total Value of Telecom Potential Obtained through CTE Model

Table 9. The total potential of the observed telecom (according to the CTE model).

I	II	III	IV	V	VI	VII	VIII	In Total
0.50061	0.2048	0.4986	0.049	0.4181	0.292	0.1998	0.30498	2.46789

shows the total potential results of the observed telecoms obtained through the CTE model.

The total value that the CTE model can give for a telecom operator is eight (without the influence of feedback, and if they are positive, this amount may be higher). Considering the obtained result shown in Table 8, it is clear that the observed telecom has low potential. In addition to assessing potential, the CTE model also provides guidelines on how and in which direction a particular telecom should be developed. Each of the areas has a maximum value of 1 (noting that, theoretically, the value can be higher if it is an “ideal” telecom where an area has a value of 1, and with the use of feedback links or the implementation of Moderate Intelligence (AI), that area can have a value greater than 1).

Here, the method of reading the value of the area will not be presented in detail, as this would require deeper analysis and clarification, but the classification by category for each of the areas is briefly listed as follows:

-

from 0 to (inclusive) 0.25—insufficient quality value

-

from 0.25 to (inclusive) 0.5—satisfactory value

-

from 0.5 to (inclusive) 0.75—good value

-

from 0.75—to (including 0.9—a very good value

-

from 0.9 to 1 (or over 1)—an excellent value

According to the results in Table 8 and the previously defined distribution, this observed telecom should definitely develop and monitor new services, increase its impact in the digital environment and improve human resources activities, and we should definitely research ways to be less dependent on external influences on business.

The overall rating shown in Table 8 is approximately 30.85% of the total maximum value (without the effect of feedback and forward links on the result), which gives an overall satisfactory rating for the observed telecom, but, with a little effort, this rating can be much improved, and, with long-term action on certain areas of the rating, can be even further improved, which will definitely bring a positive shift in telecom business, meaning more revenue, gain and greater user satisfaction.

Of course, it is up to management to determine how and at what speed this will be done. In any case, this model provides an existing assessment of the potential, but also guidelines for the development and improvement of the observed telecom.

6. Conclusions

This paper presents a new model—the Comprehensive Techno-Economic (CTE) model—for the analysis of a telecom operator’s potential. The main reason for the development of such a model was primarily the lack of a unique, simple, modular, sufficiently precise model for analysing a telecom operator’s potential.

The lack of existing models for the analysis of telecom operators is clearly explained in the introduction of this paper and in the Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F, Appendix G, Appendix H, Appendix I, Appendix G, Appendix K, Appendix L, Appendix M, Appendix N, Appendix O, Appendix P. All analyzed frameworks and models give a partial view of telecom operators. The assessment of the potential is incomplete, the models are quite complex, and, in addition, most of them rely on a significant amount of subjectivism; they also depend much on who performs the analysis.

The CTE model consists of 14 pre-defined segments joined into eight areas. Each area/segment consists of predefined items defined by mathematical equations (or otherwise defined). It is clear that in some cases the area and segment are the same. Items in areas/segments are precisely defined with the remark that items over time and through the development of the ICT business segment need to be redefined, changed and improved.

The CTE model is flexible, robust and modular. Being modular means that it can be applied as a complete model for the analysis of the potential of telecom operators, but also only individual areas or only individual parts from certain areas can be used for analysis. Flexibility and robustness mean that this model can be used to compare two or more telecom operators but also to analyze the potential of a particular telecom operator for certain purposes (e.g., for the availability of smart city services to residents, business people and visitors, etc.).

This new model represents a novel approach for the modeling and assessing of the telecom operator’s potential. This paper presents the model and its application potential. Two use cases have been described. In the first, three telecoms were compared using only two items from one area, and in the second, an analysis of the potential of one telecom was conducted.

The first example compares multiple telecoms using only some parts of the model; two items in one area. The obtained results proved its modularity. It has also been shown that the model can be used for a quick analysis and comparison using certain assumptions and approximations.

Another example is to analyze the potential of a telecom. An analysis has been made according to area and without feedback. The aim was to show how a relatively simple and fast a quality assessment of the potential of a particular telecom operator can be obtained, as well as guidelines for further development.

Both hypotheses presented in the paper are proven in Section 3 and Section 4 in the paper, where it is shown how the model is used to assess telecom potential and how it can be used modularly. A comparison of three telecoms regarding certain items of the model is also shown (therefore, modularity is also shown). According to the results, the shortcomings and advantages of telecoms were determined, as well as what needs to be paid attention to in terms of improvement and maintaining or slightly increasing the quality of telecoms.

The two scientific contributions set out to be proven in the introduction are therefore satisfied:

• A new modular model, which is developed for telecom operators, will enable objective and precise optimization of certain key and strategic technological and business decisions. This is shown throughout the entire article, and the emphasis was in Section 4, where the analysis of the potential of a telecom is presented and concrete conclusions are drawn regarding certain business and technological decisions, with the aim of better positioning in the telecom market.
• By applying the model to individual areas or even items in these areas, it is possible to obtain concrete conclusions regarding development guidelines in certain business segments. In Section 3, it was shown how the model can be used to compare three telecoms, and from that analysis how precise conclusions can be drawn regarding better mobile signal coverage within an urban area.

In any case, the paper proved the need for such a model for the needs of better development of a certain telecom, and it was established that a similar model for a quick and high-quality assessment of telecom operators does not currently exist on the market.

The CTE model is a model that is set in its structure (levels, segments and areas that are composed of segments), but also in its items within areas that are certainly changeable. Their appearance and listing will certainly change as the telecommunications market develops, and not equally in all areas; the fastest changes will be in the areas of “Product Development”, “Service Development” and “Technological and IT Development”, while in some other areas these changes will be slower and less intense (e.g., HR development). The emphasis is on the fact that this model, although it has been completed in terms of analysis, is certainly a concerning matter and requires constant monitoring of the telecom market and industry, as well as its development and improvement.

Finally, the need for such a model has been proven. Also, its potential has been demonstrated, as well as the possibility of upgrading and further research.