An Explorative Study of Mobility Adoption in the Enterprise

This research investigates the phenomenon of enterprise mobility through the lens of SAP mobile app store. SAP is a leading vendor in Enterprise Resource Planning (ERP) system, and app stores are digital platforms that provide users a central location to effectively browse, purchase, download, and update their mobile applications. By surveying the ERP mobile apps available in SAP mobile app store, this study examines the market structure of mobile apps and the factors that influence their adoption. In addition, the study incorporates the interviews of two ERP mobile app providers to help understand the influential factors in enterprise mobility adoption and the business benefits associated with enterprise mobility. Future research directions are discussed

Мобільні технології у процесі підготовки майбутніх фахівців технічних спеціальностей

На основі аналізу мобільних додатків, доступних в mobile app store, визначено можливості використання мобільного навчання у процесі підготовки майбутніх фахівців технічних спеціальностей. Мобільні додатки класифіковано за функціями. Запропоновано приклади їх використання в освітньому процесі

Application of Developers' and Users' Dependent Factors in App Store Optimization

This paper presents an application of developers' and users' dependent factors in the app store optimization. The application is based on two main fields: developers' dependent factors and users' dependent factors. Developers' dependent factors are identified as: developer name, app name, subtitle, genre, short description, long description, content rating, system requirements, page url, last update, what's new and price. Users' dependent factors are identified as: download volume, average rating, rating volume and reviews. The proposed application in its final form is modelled after mining sample data from two leading app stores: Google Play and Apple App Store. Results from analyzing collected data show that developer dependent elements can be better optimized. Names and descriptions of mobile apps are not fully utilized. In Google Play there is one significant correlation between download volume and number of reviews, whereas in App Store there is no significant correlation between factors

A Research on Fine Marketing Strategy for ZS Mobile App Store

手机应用商店是基于移动互联网技术的一种交易平台，它随着移动互联网的出现而出现，第一个手机应用商店是苹果的AppStore，之后市场上出现了各种类型的应用商店，包括手机终端厂商开发的应用商店、手机系统生产商开发的应用商店、移动运营商开发的应用商店以及第三方独立应用商店。本文研究的对象ZS应用商店属于第三方独立应用商店。 随着移动应用的增多，应用商店对于应用开发者和移动设备用户的作用越来越大，应用商店逐渐成为移动互联网产业链中的一个重要环节。由于应用商店在产业链中的重要地位、清晰的商业模式、较强的变现能力，应用商店在资本市场上广受青睐，不论是互联网巨头还是一些传统行业的大资本，都希望涉足应用商店...Mobile application store, a transaction platform based on mobile internet technology, arose with the mobile internet. With the appearance of Apple AppStore, all kinds of application store are shown up on the market, including the application stores developed by mobile phone vendor, operation system developer, mobile carrier or independent third party. ZS, being studied in this thesis, is an indepe...学位：工商管理硕士院系专业：管理学院_工商管理硕士(MBA)学号：1792010115083

Canalization or Increased Diffusion? An Empirical Analysis on the Impact of the Recommendation System in the Mobile App Market

Online retailers have increasingly adopted product recommendation systems as an effective tool to improve product visibility and promote sales. This study examines the impact of the recommendation system in the popular Google Play mobile app store. By analyzing a 60-day panel dataset with 235,638 observations from 9,735 apps, we investigate how the characteristics of the recommended apps relative to those of the focal apps affect the adoption of mobile apps in this volatile market. Our results show that the relative strength of the recommended apps over the focal app plays a key role in influencing the outcome of recommendations. Moreover, the heterogeneity of the recommendations as represented by the diversity of the popularity of the recommended apps is positively associated with a more even distribution of revenue in the market. These findings provide insights for mobile app market operators to enhance the design of their recommendation system

Never-ending Learning of User Interfaces

Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most models rely on datasets that are collected and labeled by human crowd-workers, a process that is costly and surprisingly error-prone for certain tasks. For example, it is possible to guess if a UI element is "tappable" from a screenshot (i.e., based on visual signifiers) or from potentially unreliable metadata (e.g., a view hierarchy), but one way to know for certain is to programmatically tap the UI element and observe the effects. We built the Never-ending UI Learner, an app crawler that automatically installs real apps from a mobile app store and crawls them to discover new and challenging training examples to learn from. The Never-ending UI Learner has crawled for more than 5,000 device-hours, performing over half a million actions on 6,000 apps to train three computer vision models for i) tappability prediction, ii) draggability prediction, and iii) screen similarity

Telling an Attractive Digital Story: Unraveling the Effects of Digital Product Placement Strategy on Product Exposure

The accelerated pace with which mobile apps are being launched has translated into an innovation diffusion paradox for mobile app stores. To cope with the avalanche of newly launched apps, conventional product promotion has given way to digital storytelling as a means of bolstering individuals’ exposure to these apps. Digital storytelling, as an emerging and novel format of product placement, has been credited for boosting consumers’ receptivity to featured products through compelling narrative, direct links, and rich media. In this study, we construct and empirically validate a research model that illustrates how digital storytelling can be strategized for product promotion in mobile app stores. In so doing, we endeavor to not only offer an in-depth appreciation of how digital storytelling can aid in promoting mobile apps through the presentation of engaging content but to also shed light on how these promotional effects could be moderated through rich delivery

Detection of spam review on mobile app stores, evaluation of helpfulness of user reviews and extraction of quality aspects using machine learning techniques

As mobile devices have overtaken fixed Internet access, mobile applications and distribution platforms have gained in importance. App stores enable users to search and purchase mobile applications and then to give feedback in the form of reviews and ratings. A review might contain critical information about user experience, feature requests and bug reports. User reviews are valuable not only to developers and software organizations interested in learning the opinion of their customers but also to prospective users who would like to find out what others think about an app. Even though some surveys have inventoried techniques and methods in opinion mining and sentiment analysis, no systematic literature review (SLR) study had yet reported on mobile app store opinion mining and spam review detection problems. Mining opinions from app store reviews requires pre-processing at the text and content levels, including filtering-out nonopinionated content and evaluating trustworthiness and genuineness of the reviews. In addition, the relevance of the extracted features are not cross-validated with main software engineering concepts. This research project first conducted a systematic literature review (SLR) on the evaluation of mobile app store opinion mining studies. Next, to fill the identified gaps in the literature, we used a novel convolutional neural network to learn document representation for deceptive spam review detection by characterizing an app store review dataset which includes truthful and spam reviews for the first time in the literature. Our experiments reported that our neural network based method achieved 82.5% accuracy, while a baseline Support Vector Machine (SVM) classification model reached only 70% accuracy despite leveraging various feature combinations. We next compared four classification models to assess app store user review helpfulness and proposed a predictive model which makes use of review meta-data along with structural and lexical features for helpfulness prediction. In the last part of this research study, we constructed an annotated app store review dataset for the aspect extraction task, based on ISO 25010 - Systems and software Product Quality Requirements and Evaluation standard and two deep neural network models: Bi-directional Long-Short Term Memory and Conditional Random Field (Bi-LSTM+CRF) and Deep Convolutional Neural Networks and Conditional Random Field (CNN+CRF) for aspect extraction from app store user reviews. Both models achieved nearly 80% F1 score (the weighted average of precision and recall which takes both false positives and false negatives into account) in exact aspect matching and 86% F1 score in partial aspect matching

Mobile app recommendations using deep learning and big data

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMRecommender systems were first introduced to solve information overload problems in enterprises. Over the last decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media and mobile app stores. Several methods have been proposed over the years to build recommender systems. The most popular approaches are based on collaborative filtering techniques, which leverage the similarities between consumer tastes. But the current state of the art in recommender systems is deep-learning methods, which can leverage not only item consumption data but also content, context, and user attributes. Mobile app stores generate data with Big Data properties from app consumption data, behavioral, geographic, demographic, social network and user-generated content data, which includes reviews, comments and search queries. In this dissertation, we propose a deep-learning architecture for recommender systems in mobile app stores that leverage most of these data sources. We analyze three issues related to the impact of the data sources, the impact of embedding layer pretraining and the efficiency of using Kernel methods to improve app scoring at a Big Data scale. An experiment is conducted on a Portuguese Android app store. Results suggest that models can be improved by combining structured and unstructured data. The results also suggest that embedding layer pretraining is essential to obtain good results. Some evidence is provided showing that Kernel-based methods might not be efficient when deployed in Big Data contexts