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The Determination of a GSM Operator Preference with a Multiple Logistic Regression Analysis

A Youth-Oriented Study at a University in Turkey

Bibliographic Data

ID12968994
AuthorsEmre Yakut (0000-0002-1978-0217, Osmaniye Korkut Ata University, corresponding author), Ayhan Demirci (0000-0003-3788-4586, Toros University), Erhan Ergin (0000-0003-1202-6446, Osmaniye Korkut Ata University)
Year2016
Volume8
Issue3
Pages316-316
Publication date2016-08-11
Open AccessYes
TypeARTICLE
VenueReview of European Studies (JOURNAL)
Journal identifiersISSN: 1918-7173 • E-ISSN: 1918-7181
PublisherCanadian Center of Science and Education (PUBLISHER)
DOI10.5539/res.v8n3p316
OpenAlexW2512831289
LanguageEN
References cited2

The magnitude of technological developments can be better understood nowadays in line with the developments particularly in the communication and mobile phone industries. These developments do not only trigger the production of new technology for mobile phones but they also bring forward the issue of conveying this technology to users. The applications and services produced for mobile phones are generally introduced to the market via Global System for Mobile Communications (GSM) operators providing communication services. Users’ benefiting from these applications and services in their mobile phones or from their operators, depends on their preference of a GSM operator that has the capacity to meet the users’ needs. This study has aimed to determine the factors that influence the preference of a mobile phone operator among university students, the most active age group using mobile phones. Both factor analysis and multiple logistic regression analysis are used to determine the influential factors and the degree of influence in the preference of a GSM operator. It has been identified that the most influential variable on the preference of a GSM operator is the communication expenses as a socio-economic factor, whereas the brand factor is the most important variable in deciding which GSM operator service to purchase

Business · GSM · GSM services · Logistic regression · Mobile phone · Mobile radio · Mobile telephony · Operator (biology · Phone · Preference · Service (business · Statistics · Telecommunications · Variable (mathematics · Computer Science · Consumer Market Behavior and Pricing · Innovation Diffusion and Forecasting · Mathematics · Technology Adoption and User Behaviour · Marketing

  • Logistic Regression

    Open Access•David G Kleinbaum, Mitchel Klein•Logistic Regression•2010

  • Applied Logistic Regression

    David W Hosmer, Stanley Lemeshow•Applied logistic regression•2000

Highly citedNo

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