scholarly journals Factors affecting the adoption of mobile applications by farmers: An empirical investigation

2021 ◽  
Vol 17 (1) ◽  
pp. 19-29
Author(s):  
Okoroji Victor ◽  
J Lees Nic ◽  
Lucock Xiaomeng
Keyword(s):  
Mobile Applications ◽  
Empirical Investigation ◽  
Factors Affecting

Classifying Mobile Applications Using Word Embeddings

2022 ◽  
Vol 31 (2) ◽  
pp. 1-30
Author(s):  
Fahimeh Ebrahimi ◽  
Miroslav Tushev ◽  
Anas Mahmoud
Keyword(s):  
Mobile Applications ◽  
Empirical Investigation ◽  
Mobile Apps ◽  
Human Subjects ◽  
Sharing Economy ◽  
Support Vector ◽  
Word Embeddings ◽  
Mobile Software ◽  
Vector Machines ◽  
Modern Application

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.

Factors Affecting the Adoption of Entertainment Mobile Applications in Iran

2016 ◽  
Vol 24 (4) ◽  
pp. 67-79
Author(s):  
Sina Baghbaniyazdi ◽  
Amir Ekhlassi ◽  
Kamal Sakhdari
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Valuable Insight ◽  
Theory Approach ◽  
The Novel ◽  
Structured Interviews ◽  
Application Development ◽  
Factors Affecting ◽  
Whole Process ◽  
Research On Application

While previous research on application adoption has partly advanced our understanding of factors affecting the adoption of mobile applications, less attention has been given to the whole process of application development, from idea characteristics to the supportive activities after launch. In particular, less is known about these factors in the context of developing countries, where mobile applications are gaining increasing popularity. In this vein, this paper, adopting a grounded theory approach, aims to identify factors influencing the adoption of entertainment mobile application from the developer's points of view focusing on different phases of application development in the novel context of Iran. The authors' in-depth semi-structured interviews with experts in mobile application development firms indicate 15 factors classified within four categories based on the application development process, entailing idea characteristics, design, marketing communications and supportive activities. The implications of these findings provides valuable insight into why some mobile applications are more successful than others.

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