The Impact of Temporal Context on Mobile App Usage: A Linear Regression Approach

Abstract

The recent increased popularity of the use of mobile apps has led to aggressive study of what makes user engagement and interaction. Temporal is one of the major areas that hasn Bow been well studied in terms of time of the day, day of the week or seasonal differences affecting the use of mobile applications. In this study, the hypothesis is to analyse how the context of time plays a part in predicting usage of mobile apps by using linear regression models. The main aim is to discover temporal variables, which can be considered an important aspect and to determine its connection with the frequency of use, the duration of the session, as well as the type of application. The authors utilize a dataset containing records on user activities in one of the most popular mobile apps over a six months period to create a linear regression model. Findings suggest that hour of the day and weekday are other key factors impacting user engagement and that there are definite surges in the evening and weekends time. The results indicate that by profiling user experiences based on such temporal patterns developers of apps can optimize notifications and delivery of content to users. The study can be one addition to the rising number of studies on mobile app analytics and can also serve as a guide to investigators and other applications developers who may consider using temporal context to enhance user retention.

Keywords: The usage of mobile Apps, Time-related Context, Linear Regression, User Interaction, Data mining, Forecasts