Personal and social predictors of use and non-use of fitness/diet app: Application of Random Forest algorithm

This study investigated the various groups of factors that predict individuals’ use and non-use of fitness and diet apps on smartphones. Unlike previous research on fitness and diet apps which have mainly studied individuals’ intentions to use the apps, this study focused on the prediction accuracy of various factors that lead people to use fitness and diet apps through analysis of data collected from users as well as non-users of these apps. To examine prediction accuracy, this study applied the Random Forest algorithm. According to the findings, prediction accuracy higher than that of 70 percent was observed for nine factors: age, annual income, education, perceived obesity, dieting efforts, number of smartphone apps currently used, daily time spent with smartphone apps, perceived benefits from exercise, and social influence. A major contribution of this study is its detection of those factors predicting actual behavioral decisions regarding use of fitness and diet apps, as opposed to future intentions..     

Analysis of Key Attributes Influencing the User Satisfaction towards Applications

This research aims to find out the key attributes influencing the user trust towards applications on the mobile application store. In this research, both the experimental data set and the evaluation results of analysis are described. The analysis results include the test results of analysis of variance (ANOVA) and correlation analysis. In ANOVA, the attributes affecting user rating are derived and applied to the correlation analysis to evaluate the degree of correlation towards user rating, and in the last stage the positive or negative relationship with user satisfaction is figured out.     

Starring in your own Snapchat advertisement: Influence of self-brand congruity,self-referencing and perceived humor on brand attitude and purchase intention of advertised brands

Snapchat geofilter advertisements use augmented reality (AR) technology to place consumers in photographs embellished with branded content. This study examined the joint influence of self-brand congruity, self-referencing and perceived humor in these self-endorsed geofilter brand advertisements on consumers’ brand-related preferences. Results revealed that self-brand congruity, self-referencing and perceived humor exerted significant main effects on consumers’ post-use brand attitude and purchase intention. Self-brand congruity also significantly interacted with self-referencing and perceived humor to affect brand attitude and purchase intention, while self-referencing significantly interacted with perceived humor to affect purchase intention, but not brand attitude. Theoretical and managerial implications of the research are discussed.     

How informed are messaging app users about politics? A linkage of messaging app use and political knowledge and participation

Mobile messaging apps, such as Snapchat, Facebook Messenger, and WhatsApp, were unique campaign and information platforms in the 2016 U.S. presidential election. This study assesses how using such apps for campaign information is related to political knowledge and participation. Data from an online survey conducted prior to the election indicate that using messaging apps for news is positively related to knowledge miscalibration. Knowledge miscalibration is positively related to offline and online political participation. Findings are discussed in terms of the role of messaging apps in the political process.     

Analysis of Key Attributes Influencing the User Satisfaction towards Applications

This research aims to find out the key attributes influencing the user trust towards applications on the mobile application store. In this research, both the experimental data set and the evaluation results of analysis are described. The analysis results include the test results of analysis of variance (ANOVA) and correlation analysis. In ANOVA, the attributes affecting user rating are derived and applied to the correlation analysis to evaluate the degree of correlation towards user rating, and in the last stage the positive or negative relationship with user satisfaction is figured out.     

“互联网+”时代的出租车资源配置问题评析

本文针对2015年全国大学生数学建模竞赛的B题“互联网+”时代的出租车资源配置问题,根据评阅和评奖的具体情况,首先介绍了问题的背景、提法和评阅要点,然后给出了问题的几种有代表性解决方法和模型,最后对参赛论文中存在的较普遍的问题做了分析。     

Role of location-based mobile apps in city marketing: Beşiktaş as a student-friendly district

ABSTRACT

In the age of globalisation and high competition among destinations, cities are empowered with new marketing tools to attract new residents and visitors while keeping their existing communities satisfied. Therefore, the main objective of the paper is to understand the role of location-based mobile applications among university students, who as members of the wired generation tend to use technology more than older generations. The secondary objective is to explore the most preferred location-based apps with their benefits and how they are used by the students to link themselves to the city and its services. Two focus groups are conducted with university students in Besiktas, which is claimed to be one of the most student-friendly environments in Istanbul. The findings reveal students’ preferences and reasoning for the use of location-based mobile apps. Apart from their satisfaction with the diversity of shopping, wayfinding, and geosocial networking apps, their concerns for digital privacy and personal data protection have become apparent. Further research is recommended for understanding the satisfaction and expectations of different user segments in the city since their evaluations might contribute to further develop and improve location-based services, and promote the city in the local and global markets.     

基于APP软件微课程的翻转教学的应用研究

本文研究利用APP软件对微课程进行智能管理,实现课外课内的学习监控,自主进行学习,并通过各种激励手段引起学生的更大兴趣和关注,从而达到学而优的目的.     

Exploring the effect of compulsive social app usage on technostress and academic performance: Perspectives from personality traits

With the rapid development of mobile communications technologies, social apps (e.g., Line, WeChat) have emerged as important communication tools. Although social apps provide people with additional convenience, overuse of such applications may have negative life effects, such as technostress and distraction. Past research has indicated that personality attributes contribute to compulsive usage. This study explores the relationships between personality attributes and compulsive usage of social apps, and examines the impact of technostress on academic performance. A total of 136 valid questionnaires were collected from university students through an online survey. Fourteen proposed hypotheses were examined using SmartPLS software. The results indicate that extraversion, agreeableness, and neuroticism have significant effects on compulsive usage of mobile social applications. Compulsive usage had a significant positive impact on technostress but did not negatively affect academic self-perception and course grades. In addition, conscientiousness significantly influenced academic self-perception. Unexpectedly, gender and number of friends had little influence on technostress or compulsive usage. The implications of these findings are discussed and directions for future research are offered.     

Automatic labeling of mobile apps by the type of psychological needs they satisfy

App usage is now a ubiquitous phenomenon, but little is known about what types of psychological needs are met by which apps. We proposed a method to label automatically mobile apps in terms of whether and to what extent they can satisfy users’ particular psychological needs. First, using the grounded theory approach, we conducted semi-structured in-depth interviews to identify types of needs associated with app usage. Substantive and theoretical coding of the data from the interviews as well as data from samples of app reviews yielded eight types of psychological needs app users had: utilitarian, low-cost, security, health, hedonic, social, cognitive, and self-actualization needs. Second, using the needs corpus (words and phrases) generated above, a classifier was trained using latent Dirichlet allocation (LDA) and support vector machine (SVM) algorithms to filter reviews in terms of whether they included needs-related comments. The classifier showed good performance. Finally, Labeled-LDA was used to automatically provide each review with multiple labels of the types of needs mentioned and the apps were analyzed for the different types of needs they satisfied.