A Social Choice Theoretic Approach for Analyzing User Behavior in Online Streaming Mobile Applications

2018 ◽  
pp. 143-151
Author(s):  
Neetu Raveendran ◽  
Kaigui Bian ◽  
Lingyang Song ◽  
Zhu Han
Keyword(s):  
Social Choice ◽  
Mobile Applications ◽  
User Behavior ◽  
Theoretic Approach ◽  
Online Streaming

Understanding user behavior with new mobile applications

2007 ◽  
Vol 16 (4) ◽  
pp. 393-412 ◽  
Author(s):  
Raquel Benbunan-Fich ◽  
Alberto Benbunan
Keyword(s):  
Mobile Applications ◽  
User Behavior

scholarly journals Quality and Use of Mobile Applications for Transportation Service: Influence on Satisfaction

2021 ◽  
pp. 21-42
Author(s):  
Claudia Leticia Preciado-Ortiz
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Structural Equation ◽  
User Behavior ◽  
Research Work ◽  
Equation Modeling ◽  
Transportation Industry ◽  
Transportation Service ◽  
Intention To Continue

The main objective of this research work was to analyze the factors that influence satisfaction and the intention to continue with the use of mobile transport applications in young university students from Guadalajara, Jalisco, Mexico. The approach was quantitative. 144 valid responses were used, and partial least squares structural equation modeling (PLS-SEM) was used to test the model. The software employing was the SmartPLS 3. The results indicate that the quality of the design, the quality of the information and the quality of the system are predictors of influence on satisfaction. Companies that offer individual passenger transport through a mobile application have increased in recent years, generating strong competition both between existing brands and with established traditional taxis. This study provides new and recent information for marketing managers and academics on application user behavior in the transportation industry.

scholarly journals Explaining user experience in mobile gaming applications: an fsQCA approach

2019 ◽  
Vol 29 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Ilias O. Pappas ◽  
Patrick Mikalef ◽  
Michail N. Giannakos ◽  
Panos E. Kourouthanassis
Keyword(s):  
Complexity Theory ◽  
Mobile Applications ◽  
Positive Emotions ◽  
User Behavior ◽  
Qualitative Comparative Analysis ◽  
Mobile Games ◽  
Content Type ◽  
Content Quality ◽  
Intention To Adopt ◽  
Research Propositions

Purpose In the complex ecosystem of mobile applications multiple factors have been used to explain users’ behavior, without though focusing on how different combinations of variables may affect user behavior. The purpose of this paper is to show how price value, game content quality, positive and negative emotions, gender and gameplay time interact with each other to predict high intention to download mobile games. Design/methodology/approach Building on complexity theory, the authors present a conceptual model followed by research propositions. The propositions are empirically validated through configurational analysis, employing fuzzy-set qualitative comparative analysis (fsQCA) on 531 active users of mobile games. Findings Findings identify ten solutions that explain high intention to download mobile games. Alternative paths are identified depending on the gender and the time users spend playing mobiles games. The authors highlight the role of price value and game content quality, as well as that of positive emotions, which are always core factors when present. Originality/value To identify complex interactions among the variables of interest, fsQCA is employed, differentiating from traditional studies using variance-based methods, leading to multiple solutions explaining the same outcome. None of the variables explains the intention to download on its own, but only when they combine with each other. The authors extend existing knowledge on how price value, game content quality, emotions, gender and gameplay time combine to lead to high intention to download mobile games; and present a methodology for how to bridge complexity theory with fsQCA, improving our understanding of intention to adopt mobile applications.

scholarly journals Growth, Adoption, and Use of Mobile E-Commerce

2014 ◽  
Vol 104 (5) ◽  
pp. 489-494 ◽  
Author(s):  
Liran Einav ◽  
Jonathan Levin ◽  
Igor Popov ◽  
Neel Sundaresan
Keyword(s):  
Mobile Applications ◽  
User Behavior ◽  
Internet Site ◽  
Internet Commerce ◽  
Early Adopters ◽  
The Core ◽  
Mobile Shopping ◽  
Internet Platform ◽  
Early Effects ◽  
Sustained Increase

We document some early effects of how mobile devices might change Internet and retail commerce. We present three main findings based on an analysis of eBay's mobile shopping application and core Internet platform. First, early adopters of mobile e-commerce applications appear to be people who already were relatively heavy Internet commerce users. Second, adoption of the mobile shopping application is associated with both an immediate and sustained increase in total platform purchasing, with little evidence of substitution from the core platform. Third, differences in user behavior across the mobile applications and the regular Internet site are not yet so dramatic.

A User Behavior Based Trust Model for Mobile Applications

2008 ◽  
pp. 455-469 ◽  
Author(s):  
Zheng Yan ◽  
Valtteri Niemi ◽  
Yan Dong ◽  
Guoliang Yu
Keyword(s):  
Mobile Applications ◽  
User Behavior ◽  
Trust Model ◽  
Behavior Based

scholarly journals Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach

2020 ◽  
Vol 10 (15) ◽  
pp. 5324 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Yong Zheng ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
User Behavior ◽  
User Preferences ◽  
Context Aware ◽  
Listening Behavior ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Listening Habits ◽  
Online Streaming ◽  
Context Free

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations.

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