A general service description data model for mobile Internet service management

2004 ◽  
Author(s):  
Man Yuan ◽  
Xinglong Wang ◽  
Rui He ◽  
Hong Zhang ◽  
Zhigang Kan ◽  
...  
Keyword(s):  
Data Model ◽  
Service Management ◽  
Mobile Internet ◽  
Service Description ◽  
Internet Service ◽  
General Service

scholarly journals Influencing Factors for the Acceptance of 3G Mobile Service in Kathmandu Valley

2018 ◽  
Vol 1 (2) ◽  
pp. 135
Author(s):  
Anij Taludhar
Keyword(s):  
Significant Influence ◽  
Service Providers ◽  
User Acceptance ◽  
Mobile Internet ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Kathmandu Valley ◽  
Mobile Service ◽  
Perceived Ease Of Use ◽  
Internet Service

<p>The mobile industry in recent years has been growing at a speedy pace where the use of cell phone is no longer limited to conventional usage like the voice communication, but has enriched the customer experiences with mobile internet services and other value added services. Along with the growing mobile industry, technology behind it is also changing accordingly. However, the user acceptance of technology depends on various factors that lead to either user acceptance or rejection. This study thus aims to identify the major determining factors that influence the user to use the 3G mobile internet and examines the interrelation among the determinants along with effect of the demographic variable limited to Kathmandu Valley users. The conceptual framework of this study is based on the extended version of Davis (1989)’s technology acceptance model using variables social influence, price perception, perceived usefulness, perceived ease of use, attitude to use and behavior intention. The primary survey is conducted using a set of questionnaire, which produced 180 valid responses from the mobile users in Kathmandu Valley. The reliability of the data from questionnaire is verified with factor analysis and Cronbach’s alpha. The result shows that social influence has significant influence on perceived ease of use while it has no significant influence on perceived usefulness. Price perception, on the other hand, influences the perceived usefulness. Similarly, perceived usefulness and perceived ease of use influence the attitude of use, which finally influences the behavioral intention. Along with this, social influence is also found to be the extended factors that can influence the attitude of the users, which in turn affects the behavioral intention. Demographic variables, however in this study, have shown no significant influence on the behavioral intentions. On the whole, the usage of 3G mobile internet service is not seen to be satisfying from the study. The major reasons as surveyed from the users themselves for this lower utilization seems to be higher data costs, presence of cheaper alternatives for internet like Wi-Fi internet, quick drainage of battery and slow mobile internet service provided by the mobile service providers. Thus, for mobile service providers, internet service providers are their direct competitors for 3G mobile internet services. This paper enhances understanding of user acceptance of 3G mobile internet services.</p><p>Journal of Business and Social Sciences Research, Vol. 1, No. 2, pp. 135-152</p>

A Cross-National Study of Mobile Internet Services

2009 ◽  
Vol 17 (4) ◽  
pp. 29-54 ◽  
Author(s):  
Dong Hee Shin
Keyword(s):  
Structural Equation ◽  
Uses And Gratifications ◽  
The United States ◽  
Mobile User ◽  
Mobile Internet ◽  
Relational Model ◽  
Equation Modeling ◽  
National Study ◽  
Mobile Service ◽  
Internet Service

This study surveyed mobile users in the United States and Korea to determine the key differences between the two countries. Survey questions, developed in two languages, were presented in each country to explore the influences of informativeness, entertainment, interactivity, and availability on mobile user dimensions. The study design methods were based on the revision of a uses and gratifications approach, and a relational model of antecedents and consequences was tested with a structural equation modeling approach. Mobile Internet service uses and gratifications were analyzed cross-nationally in a comparative fashion focusing on the differences in the composition of motives in the two countries. Based on the results of this study, practical implications for marketing strategies in mobile service markets and theoretical implications for cross-country studies are recommended accordingly.

DragonNet: A robust mobile Internet service system for long distance trains

2011 ◽  
Author(s):  
Fung Po Tso ◽  
Lin Cui ◽  
Lizhuo Zhang ◽  
Weijia Jia ◽  
Di Yao ◽  
...  
Keyword(s):  
Service System ◽  
Mobile Internet ◽  
Long Distance ◽  
Internet Service

scholarly journals Kepuasan Pelanggan Pada Industri Penyedia Layanan Mobile Internet Di Jakarta

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ariyawira Binui Dan Eddy Herjanto
Keyword(s):  
Customer Satisfaction ◽  
Service Providers ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Mobile Internet ◽  
Primary Data ◽  
Analysis Method ◽  
Internet Service ◽  
Network Quality

The rapid advances in technology, especially in the mobile sector, make communication and access to information very easy. These things make the internet as one of the main needs of modern humans. Companies that move as internet service providers must be able to find out what factors influence customer satisfaction to increase sales and attract the services offered. This study aims to analyze what factors influence consumers in the mobile internet service provider industry in Jakarta. These factors include price suitability, network quality, and service quality. The data used in this study is primary data, which is the result of 150 respondents in Jakarta. This research took place from March 2018 to May 2018. The data analysis method used in this study was descriptive analysis method and multiple linear regression analysis. The results of the study show that these three variables have significant variables on the level of customer satisfaction. Where dimensions that have the most dominant influence are dimensions of network quality, then followed by quality dimensions.

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