Evaluation of Telecommunication Companies Using Data Envelopment Analysis: Toward Efficiency of Mobile Telephone Operator in Tanzania

Suleiman, Munir Said; Hemed, Nitham Salum; Wei, Jiuchang

doi:10.17706/ijeeee.2018.8.3.154-164

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…Telkom achieved efficiency = 1 (constant return scale) in 2019 and 2020, Link Net in 2015, Icon Plus in and 2017, Moratel in 2015and 2017, and Bali Towerindo in 2017, 2017, 2019, Link Net was one of the operators experiencing declining returns to scale (drs). It occurred to Icon Plus in 2018, 2019.…”

Section: Resultsmentioning

confidence: 99%

“…The SFA method was used by Riko et al (2019) to conduct a technical efficiency analysis, which revealed that telecommunication companies in Asia Pacific have lower technical efficiency than those in developed countries. Suleiman et al, 2017) determined that only three (three) cellular phone operators out of seven (seven) operators studied were efficient using Data Envelopment Analysis (DEA), Non-Radial and Oriented Model on Slack-based Measure (Devaki, 2020), the financing pattern of telecommunication service companies provides analysis results that describe the company's capital structure, which is important to maintain a good solvency position. Kayisire & Wei (2016) evaluated the efficiency of mobile phone operators using DEA to measure various benchmarking industries and evaluate the performance of Information and Communication Technology (ICT) adoption in Africa.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency Analysis of Fixed Broadband Service Management Industry: Measurement of the Integrated DEA Model

Rahayu

2022

EJBMR

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The goal of this study was to examine the level of efficiency of fixed broadband service providers as a measure of the research model's input as influenced by internal and external factors. Non-parametric approaches, Data Envelopment Analysis (DEA) models, and Path Analysis were used in this study to assess the efficiency of Fixed Broadband Service Providers. The reason for the study is that the ability to generate income to obtain maximum profit in order to be in a strategic position for business competitiveness is a key component to consider for the Fixed Broadband Service Provider industry in order to ensure business continuity. The research findings indicate that the input variable has an impact on the output variable. For the 2015 – 2020 period, the input size in the form of Total Assets has an impact on the Cost Flow Investing Act, total subscribers, and fiber optic backbone network, all of which affect the efficiency of Fixed Broadband Service Providers as many as 5 (five) Big Providers (The Big Five). External factors such as Customer Price Index (CPI) and Interest Rates, on the other hand, have a favorable impact on Operator Efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of Fixed Broadband Service Management Industry: Measurement of the Integrated DEA Model

Rahayu

2022

EJBMR

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“…Many studies have worked on the performance evaluation of MNOs. An example is the performance evaluation of Tanzania's MNOs (Sulaiman, Hemed, & Wei, 2018). The study evaluated the performance of MNOs using Data Envelopment Analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Mobile Network Operators Based on Input-Oriented Constant Return to Scale Model

Ishaq¹,

Ajibesin

2022

International Journal of E-Services and Mobile Applications

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This research evaluated the Performance of major Mobile Network Operators (MNOs) in Nigeria using Data Envelopment Analysis (DEA). The DEA method that is based on Input-oriented Constant Return to Scale (CRS) model is considered. The objectives of this research were to analyze existing methods used for the evaluation of MNOs' performance and explored DEA which is a non-parametric method that relies on a linear programming technique for optimization. The aim is to evaluate the technical efficiency (TE) of each network operator and consequently determine their ranking. The result obtained shows the distributions of MNOs that are technically efficient and those that are technically inefficient. Also, the ranking according to their relative efficiency was presented. The findings, which are based on the CRS model show that among the four Nigerian major MNOs examined (MTN, 9mobile, Glo, and Airtel), 9mobile ranked the highest efficient mobile operator followed by the MTN, then GLO while Airtel has the lowest rank.

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“…Additionally, Suleiman, Hemed and Wei (2017) evaluate the efficiency of 7 mobile operators in Tanzania from 2010 to 2016. The methodology applied was the Non-Radial Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) in each operator.…”

Section: Literature Reviewmentioning

confidence: 99%

Telecommunication Sector Reform in Southeast Asia: A New Rationality

Hendrawan

Nugroho

2018

Global J. Bus. Soc. Sci. Review

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Objective - The telecommunications industry in Southeast Asia continues to grow, as evidenced by the market penetration that continues to increase from 3.8% in 2000 to 71.1% in 2017. However, although the number of subscribers and revenue from telecommunication operators continued to grow between 2008 to 2017, the data shows that the EBITDA margin and ARPU value decrease with the growth rate (CAGR) -1.12% and -4.42%. Methodology/Technique - This study measures and analyzes the efficiency of 14 operators in Southeast Asia between 2008 to 2017 by using the Data Envelopment Analysis (DEA) method. Findings - The results show that of the 14 telecom operators examined, Telkomsel (Indonesia) was the most efficient operator with an efficiency value of 0.97 and contributed from subscriber and revenue output variables which far exceeded the average of other operators. The most efficient telecommunication companies thereafter were StarHub (Singapore) and SingTel (Singapore) with an efficiency rating of 0.96 and 0.95 as represented from ARPU output variables that far exceeded the average of other operators. Novelty - True Move operators became the telecommunication companies with the lowest efficiency because the value of its output variables (subscribers, revenue and ARPU) was far below average and did not show any positive correlation to its efficiency value. Type of Paper - Empirical. Keywords: Data Envelopment Analysis (DEA); Efficiency; Telecommunication Industry; Southeast Asia. JEL Classification: M1, M10, M19.

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Evaluation of Telecommunication Companies Using Data Envelopment Analysis: Toward Efficiency of Mobile Telephone Operator in Tanzania

Cited by 5 publications

References 33 publications

Efficiency Analysis of Fixed Broadband Service Management Industry: Measurement of the Integrated DEA Model

Efficiency Analysis of Fixed Broadband Service Management Industry: Measurement of the Integrated DEA Model

Performance Evaluation of Mobile Network Operators Based on Input-Oriented Constant Return to Scale Model

Telecommunication Sector Reform in Southeast Asia: A New Rationality

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