A Time-Dependent Principal Components-Based Dimension Reduction Approach to Analyzing the Influence of Product Interventions on User Engagement with Mobile Applications

2018 ◽  
Author(s):  
Lior Turgeman ◽  
Otis Smart
Keyword(s):  
Dimension Reduction ◽  
Principal Components ◽  
Mobile Applications ◽  
Time Dependent ◽  
User Engagement ◽  
Reduction Approach

scholarly journals A Dimension Reduction Approach to Player Rankings in European Football

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 119503-119519
Author(s):  
Ayse Elvan Aydemir ◽  
Tugba Taskaya Temizel ◽  
Alptekin Temizel ◽  
Kliment Preshlenov ◽  
Daniel M. Strahinov
Keyword(s):  
Dimension Reduction ◽  
European Football ◽  
Reduction Approach

scholarly journals Development of a Multidimensional App-Quality Assessment Tool for Health-Related Apps (AQUA)

2020 ◽  
Vol 1 (2) ◽  
pp. 13-23
Author(s):  
Teresa O'Rourke ◽  
Rüdiger Pryss ◽  
Winfried Schlee ◽  
Thomas Probst
Keyword(s):  
Quality Assessment ◽  
Mobile Applications ◽  
Assessment Tool ◽  
Cognitive Testing ◽  
Assessment Tools ◽  
German Language ◽  
User Engagement ◽  
Quality Assessment Tool ◽  
The Public ◽  
Health Related

Background A multitude of health-related mobile applications is available to the public in app stores. Many of these apps were not developed by health professionals and do not keep what they promise. To facilitate a safe handling and use of such apps, it is important to assess their quality in a standardized way. Some instruments for app quality assessment exist, although they have some limitations, which we want to improve on with this new assessment tool. Objectives The objective of this paper is to introduce a new multidimensional criteria-based tool for the quality assessment of health-related apps. Method Based on existing app-quality assessment tools and guidelines for evaluating health-related app-quality, items were constructed to assess objective and subjective app-quality. A pretest in form of cognitive testing was conducted with six participants and some items were optimized. Results An expert and a user version of AQUA were developed in English and German language. The expert version consists of 31 items in the seven dimensions Usability, User Engagement, Content, Visual Design, Therapeutic Quality, Security and Information. The user version consists of 34 items and additionally includes the dimension Impact. Conclusion AQUA is a brief multidimensional app-quality assessment tool that can be used for the quality assessment of health-related and mental health-related apps by experts and app-users.

Modelling and predicting User Engagement in mobile applications

Data Science ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 61-77
Author(s):  
Eduardo Barbaro ◽  
Eoin Martino Grua ◽  
Ivano Malavolta ◽  
Mirjana Stercevic ◽  
Esther Weusthof ◽  
...  
Keyword(s):  
Random Forest ◽  
Mobile Applications ◽  
Negative Binomial ◽  
Numerical Models ◽  
Cox Proportional Hazards ◽  
Mobile App ◽  
User Engagement ◽  
Tree Model ◽  
Clustering Model ◽  
Boosted Tree

The mobile ecosystem is dramatically growing towards an unprecedented scale, with an extremely crowded market and fierce competition among app developers. Today, keeping users engaged with a mobile app is key for its success since users can remain active consumers of services and/or producers of new contents. However, users may abandon a mobile app at any time due to various reasons, e.g., the success of competing apps, decrease of interest in the provided services, etc. In this context, predicting when a user may get disengaged from an app is an invaluable resource for developers, creating the opportunity to apply intervention strategies aiming at recovering from disengagement (e.g., sending push notifications with new contents).In this study, we aim at providing evidence that predicting when mobile app users get disengaged is possible with a good level of accuracy. Specifically, we propose, apply, and evaluate a framework to model and predict User Engagement (UE) in mobile applications via different numerical models. The proposed framework is composed of an optimized agglomerative hierarchical clustering model coupled to (i) a Cox proportional hazards, (ii) a negative binomial, (iii) a random forest, and (iv) a boosted-tree model. The proposed framework is empirically validated by means of a year-long observational dataset collected from a real deployment of a waste recycling app. Our results show that in this context the optimized clustering model classifies users adequately and improves UE predictability for all numerical models. Also, the highest levels of prediction accuracy and robustness are obtained by applying either the random forest classifier or the boosted-tree algorithm.

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