Adoption of mobile Location-Based Services with Zaltman Metaphor Elicitation Techniques

2009 ◽  
Vol 7 (1) ◽  
pp. 117 ◽  
Author(s):  
Tzong Ru Lee ◽  
Shiou Yu Chen ◽  
Shiau Ting Wang ◽  
Shuchih Ernest Chang
Keyword(s):  
Location Based Services ◽  
Mobile Location

User Assessment as Input for Useful Geospatial Representations within Mobile Location-Based Services

2007 ◽  
Vol 11 (2) ◽  
pp. 283-309 ◽  
Author(s):  
Karen Wealands ◽  
Peter Benda ◽  
Suzette Miller ◽  
William E Cartwright
Keyword(s):  
Location Based Services ◽  
Mobile Location ◽  
User Assessment

Mobile Location Based Services

2014 ◽  
pp. 214-227
Author(s):  
Hee Jhee Jiow
Keyword(s):  
Mobile Network ◽  
Location Based Services ◽  
Mobile Service ◽  
Positioning System ◽  
System Technology ◽  
Mass Market ◽  
Mobile Location ◽  
Global Positioning ◽  
The World ◽  
Evolution Of Technology

Mobile Location Based Services (MLBS) have been in operation since the 1970s. Conceived initially for military use, the Global Positioning System technology was later released to the world for other applications. As usage of the technology increased, mobile network points, developed by mobile service operators, supplemented its usage in various applications of MLBS. This chapter charts the trajectory of MLBS applications in the mass market, afforded by the evolution of technology, digital, and mobility cultures. Assimilating various MLBS classifications, it then situates examples into four quadrants according to the measures of user-position or device-position focus, and alert-aware or active-aware applications. The privacy implications of MLBS are captured on the economic, social, and political fronts, and its future is discussed.

Towards a Classification Framework for Mobile Location Based Services

2011 ◽  
pp. 67-85 ◽  
Author(s):  
George M. Giaglis ◽  
Panos Kourouthanassis ◽  
Argiros Tsamakos
Keyword(s):  
Business Models ◽  
New Technologies ◽  
Geographical Location ◽  
Mobile User ◽  
Location Based Services ◽  
Location Services ◽  
Mobile Location ◽  
Classification Framework ◽  
Future Potential ◽  
Pricing Schemes

The emerging world of mobile commerce is characterized by a multiplicity of exciting new technologies, applications, and services. Among the most promising ones will be the ability to identify the exact geographical location of a mobile user at any time. This ability opens the door to a new world of innovative services, which are commonly referred to as Mobile Location Services (MLS). This chapter aims at exploring the fascinating world of MLS, identifying the most pertinent issues that will determine its future potential, and laying down the foundation of a new field of research and practice. The contribution of our analysis is encapsulated into a novel classification of mobile location services that can serve both as an analytical toolkit and an actionable framework that systemizes our understanding of MLS applications, underlying technologies, business models, and pricing schemes.

Drivers and Inhibitors of Mobile-Payment Adoption by Smartphone Users

2012 ◽  
Vol 8 (3) ◽  
pp. 50-67 ◽  
Author(s):  
Pavel Andreev ◽  
Nava Pliskin ◽  
Sheizaf Rafaeli
Keyword(s):  
Perceived Risk ◽  
Negative Impact ◽  
Positive Impact ◽  
Conceptual Modeling ◽  
Location Based Services ◽  
Mobile Payment ◽  
Research Model ◽  
Mobile Location ◽  
Smart Mobile ◽  
Conceptual Research

The widespread penetration of smart mobile devices has facilitated rapid growth of mobile location-based services (LBS), which provide users with a variety of benefits and are attractive from a marketing perspective. However, mobile-payment (M-Payment) adoption by users has been below expectations. For better understanding of drivers and inhibitors of the willingness to M-Pay for mobile LBS, this study contributes by conceptual modeling and empirical assessment of user willingness to M-Pay. To test the proposed conceptual research model, data from 122 valid responses were analyzed by employing the Partial Least Squares (PLS) technique. The findings show that Perceived Risk is the main inhibitor of user willingness to M-Pay for LBS and that the magnitude of this inhibitor’s negative impact is at least twice the magnitude of any driver’s positive impact.

scholarly journals Examination of Location Based Services and User Preferences

2019 ◽  
Vol 9 (2S3) ◽  
pp. 342-346
Keyword(s):  
Optimal Solution ◽  
User Preferences ◽  
Location Based Services ◽  
Mobile Location ◽  
User Interests ◽  
Location Data ◽  
Start Up ◽  
Recent Developments ◽  
Store Location ◽  
Year 2000

Recent developments on mobile location information have driven efforts to mine user patterns of interest. Even start-up companies survey user interests to enrich their business. All medium and large organizations are paying attention to collect and store location data. With the support of unlimited computing power and memory of mobile phones we can apply proficient Deep Learning algorithms to determine an optimal solution for user interests. In this article, we aim to complete an overall survey on evolution of Location Based Services and the improvements in recent trends. We have categorized the evolution period in to three divisions covering from the year 2000 to till date.

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