An empirical study of emergency updates for top android mobile apps

The mobile app market continues to grow at a tremendous rate. The market provides a convenient and efficient distribution mechanism for updating apps. App developers continuously leverage such mechanism to update their apps at a rapid pace. The mechanism is ideal for publishing emergency updates (i.e., updates that are published soon after the previous update). In this paper, we study such emergency updates in the Google Play Store. Examining more than 44,000 updates of over 10,000 mobile apps in the Google Play Store, we identify 1,000 emergency updates. By studying the characteristics of such emergency updates, we find that the emergency updates often have a long lifetime (i.e., they are rarely followed by another emergency update). Updates preceding emergency updates often receive a higher ratio of negative reviews than the emergency updates. However, the release notes of emergency updates rarely indicate the rationale for such updates. Hence, we manually investigate the binary changes of several of these emergency updates. We find eight patterns of emergency updates. We categorize these eight patterns along two categories “Updates due to deployment issues” and “Updates due to source code changes”. We find that these identified patterns of emergency updates are often associated with simple mistakes, such as using a wrong resource folder (e.g., images or sounds) for an app. We manually examine each pattern and document its causes and impact on the user experience. App developers should carefully avoid these patterns in order to improve the user experience.     

Applications of mobile apps in the medical service system

Microsystem Technologies - With the prevalence of the Internet, the application of information technologies in everyday life is becoming increasingly common. Hospitals are no exception. In the...     

Recommendation algorithm of the app store by using semantic relations between apps

In this paper, we propose a personalized recommendation system for mobile application software (app) to mobile user using semantic relations of apps consumed by users. To do that, we define semantic relations between apps consumed by a specific member and his/her social members using Ontology. Based on the relations, we identify the most similar social members from the reasoning process. The reasoning is explored from measuring the common attributes between apps consumed by the target member and his/her social members. The more attributes shared by them, the more similar is their preference for consuming apps. We also develop a prototype of our system using OWL (Ontology Web Language) by defining ontology-based semantic relations among 50 mobile apps. Using the prototype, we showed the feasibility of our algorithm that our recommendation algorithm can be practical in the real field and useful to analyze the preference of mobile user.     

User adoption and purchasing intention after free trial: an empirical study of mobile newspapers

Marketing stimuli such as free trial has been widely used to increase user acceptance and intention to purchase information services. Information technology (IT) acceptance theories, such as the technology acceptance model and the unified theory of acceptance and use of technology, have been widely used to explain information system (IS) usage. These theories, however, do not explicitly consider the effect of marketing stimuli that would influence and shape user beliefs, attitude and behavior towards the use and purchase of new IS/IT. Echoing calls for advancing knowledge in technology acceptance, we propose a theoretical model based on expectation conformation theory to investigate the effect of marketing stimuli in the form of free trial and price of using IS on consumers’ acceptance decision process. In this study, free trial of mobile newspaper is used as the research context. A survey sample of 192 responses is used to test the model. Results suggest that the trial experience has an impact on post-trial beliefs and attitude. Perceived fee also has an effect on the acceptance of the information service when the users need to pay for the service.     

A study of the relation of mobile device attributes with the user-perceived quality of Android apps

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users may rate apps on a five point (star) scale where a five star-rating is the highest rating. Having considered the importance of a high star-rating to the success of an app, recent studies continue to explore the relationship between the app attributes, such as User Interface (UI) complexity, and the user-perceived quality. However, the user-perceived quality reflects the users’ experience using an app on a particular mobile device. Hence, the user-perceived quality of an app is not solely determined by app attributes. In this paper, we study the relation of both device attributes and app attributes with the user-perceived quality of Android apps from the Google Play Store. We study 20 device attributes, such as the CPU and the display size, and 13 app attributes, such as code size and UI complexity. Our study is based on data from 30 types of Android mobile devices and 280 Android apps. We use linear mixed effect models to identify the device attributes and app attributes with the strongest relationship with the user-perceived quality. We find that the code size has the strongest relationship with the user-perceived quality. However, some device attributes, such as the CPU, have stronger relationships with the user-perceived quality than some app attributes, such as the number of UI inputs and outputs of an app. Our work helps both device manufacturers and app developers. Manufacturers can focus on the attributes that have significant relationships with the user-perceived quality. Moreover, app developers should be careful about the devices for which they make their apps available because the device attributes have a strong relationship with the ratings that users give to apps.     

Adoption of mobile ICT for health promotion: an empirical investigation

This research is an unbiased empirical evaluation of user reasons to accept or resist a mobile information and communication technology (ICT) application for health promotion. This innovative use of mobile ICT consists of developing services that educate people to stay healthy, with clear benefits for both individuals and society. Receiving customized health advice through mobile devices may be an attractive service. However, despite their ability to support users, mobile services may sometimes irritate by being too intrusive. A 1-month experiment exposed participants to a health promotion application, delivered through their cell phones. This was the framework for the evaluation of an adoption model that included both positive and negative user adoption factors. Findings revealed intrinsic motivation to be a sufficient reason for adoption and a multi-faceted perceived overall risk factor as the main obstacle. Accordingly, when usefulness is less apparent, enjoyment may be a key factor for the adoption of mobile ICT for health promotion.     

Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China

Unlike traditional technologies, the use of mobile technology is exposed to shifting use contexts. Use context has frequently been described as an important factor influencing the adoption of mobile innovations. However, empirical evidence about the impact of use context is limited. This paper investigated the effect of use context on the formation of users’ perceptions of mobile hedonic services by using mobile gaming as an example. Through the employment of structural equation modelling technology, an adoption model of mobile gaming is proposed and assessed based on results from 267 questionnaires. The results show that use context is the strongest predictor of mobile game adoption. It directly or indirectly affects all different perceptions of mobile gaming in significant ways, including perceived ease of use, perceived usefulness, perceived enjoyment, cognitive concentration, attitude and behavioral intention. Additionally, perceived usefulness, perceived enjoyment and cognitive concentration all have a positive influence on the attitudinal variables of mobile game acceptance. We concluded that the formation of people’s perceptions about mobile gaming is conditional and based on the special consideration of certain use contexts. Both theoretical and practical implications are discussed.     

An empirical study on trust in mobile banking: A developing country perspective

Trust is essential for Mobile Banking (MB) adoption and usage. MB technology has the potential to improve people's quality of life and to bring efficiency to banks. In this paper, MB trust was addressed in Brazil, a developing country that has an enormous potential for expansion of banking services. We used Confirmatory Factor Analysis and Structural Equation Modeling to analyze the database, which was composed of 1077 questionnaires. In this study sample, determinants of trust had similar behavior when compared to determinants of trust previously pointed out in the literature. Our discussion indicated a kind of information asymmetry that could be mitigated in order to build trust in MB and promote its adoption. However, we observed a negative relationship between trust in MB and undergraduate course area (dummy variable for undergraduate courses in technology). The inclusion and analysis of this new variable, especially in developing countries, may contribute with the literature on MB adoption.     

Role of user a priori attitude in the acceptance of mobile health: an empirical investigation

The objective of this research is to investigate the role of user attitude toward the activity supported by a mobile health application in the overall technology acceptance equation. For that, a perceived risk-motivation theoretical model integrating user attitude on quitting smoking was developed and tested empirically with 170 participants from the UK for the context of using cell phones to support smoking cessation interventions. Results show an attitude favourable to quitting smoking has a negative effect on the perceived risk, no significant effect on the motivation, and a small positive influence on the behavioural intention associated with using the mobile health service. Overall, having a positive a priori attitude toward a healthy activity is not a sufficient reason to make users accept a mobile service supporting that activity.     

Improving multiple-password recall: an empirical study

As one of the most common authentication methods, passwords help secure information by granting access only to authorized parties. To be effective, passwords should be strong, secret, and memorable. While password strength can be enforced by automated information technology policies, users frequently jeopardize secrecy to improve memorability. The password memorability problem is exacerbated by the number of different passwords a user is required to remember. While short-term memory theories have been applied to individual-password management problems, the relationship between memory and the multiple-password problem has not been examined. This paper treats the multiple-password management crisis as a search and retrieval problem involving human beings’ long-term memory. We propose that interference between different passwords is one of the major challenges to multiple-password recall and that interference alleviation methods can significantly improve multiple-password recall. A lab experiment was conducted to examine the effectiveness of two interference alleviation methods: the list reduction method and the unique identifier method. While both methods improve multiple-password recall performance, the list reduction method leads to statistically significant improvement. The results demonstrate the potential merit of practices targeting multiple-password interference. By introducing long-term memory theory to multiple-password memorability issues, this study presents implications benefiting users and serves as the potential starting point for future research.     

The perceptions towards mobile services: an empirical analysis of the role of use facilitators

The adoption patterns for new technology such as mobile services are essential to understand for developing them further. Hence, it is important to study how willing people are to try new mobile services and do variables, which facilitate the use of the services have an effect on the consumers’ perceptions of the services and their adoption readiness. This study analyses how the familiarity of mobile devices, the time the services are initially used and users’ technology skills affect the initial mobile service usage experience and the likelihood of continuous usage. The results show that the duration of the use does not effect consumers’ perceptions about mobile services, but familiarity of the device and user skills have an impact on the perceptions of the services.     

Android mobile VoIP apps: a survey and examination of their security and privacy

Voice over Internet Protocol (VoIP) has become increasingly popular among individuals and business organisations, with millions of users communicating using VoIP applications (apps) on their smart mobile devices. Since Android is one of the most popular mobile platforms, this research focuses on Android devices. In this paper we survey the research that examines the security and privacy of mVoIP published in English from January 2009 to January 2014. We also examine the ten most popular free mVoIP apps for Android devices, and analyse the communications to determine whether the voice and text communications using these mVoIP apps are encrypted. The results indicate that most of the apps encrypt text communications, but voice communications may not have been encrypted in Fring, ICQ, Tango, Viber, Vonage, WeChat and Yahoo. The findings described in this paper contribute to an in-depth understanding of the potential privacy risks inherent in the communications using these apps, a previously understudied app category. Six potential research topics are also outlined.     

Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation

User adoption of mobile payment (m-payment) is low compared to the adoption of traditional forms of payments. Lack of user trust has been identified as the most significant long-term barrier for the success of mobile finances systems. Motivated by this fact, we proposed and tested an initial trust theoretical model for user adoption of m-payment systems. The model not only theorizes the role of initial trust in m-payment adoption, but also identifies the facilitators and inhibitors for a user’s initial trust formation in m-payment systems. The model is empirically validated via a sample of 851 potential m-payment adopters in Australia. Partial least squares structural equation modelling is used to assess the relationships of the research model. The results indicate that perceived information quality, perceived system quality, and perceived service quality as the initial trust facilitators are positively related to initial trust formation, while perceived uncertainty as the initial trust inhibitor exerts a significant negative effect on initial trust. Perceived asset specificity is found to have insignificant effect. In addition, the results show that initial trust positively affects perceived benefit and perceived convenience, and these three factors together predict usage intention. Perceived convenience of m-payment is also found to have a positive effect on perceived benefit. The findings of this study provide several important implications for m-payment adoption research and practice.     

Software architectural patterns in practice: an empirical study

Software architecture involves a series of decisions based on many factors in a wide range of software development. Architects face recurring issues in different software architecture design, and to reduce huge cost and risks, software architecture decisions can rely on a set of idiomatic patterns commonly named architectural styles or patterns. Architectural pattern determines the vocabulary of components and connectors that are used in instances of the pattern together with a set of constraints to combine the two. Little contemporary data exists to document actual practices used by software professionals when selecting and incorporating architectural patterns for their projects in industry. Therefore, a comprehensive survey of software professionals was conducted to attempt to discover these practices. This exploratory survey and its quantitative results offer opportunities for further interpretation and comparison. Data from this survey are presented in this paper and include characteristics of projects, practices, organizations, and practitioners related to the usage of architectural patterns. Some of the notable findings include that architectural patterns are widely used in software projects with the Model–View–Controller being the most common. Despite reported difficulties in incorporating architectural patterns, the majority of the software professionals revealed that patterns were the most essential for completing the projects. The most difficult pattern to implement and the most expensive to adopt was the peer-to-peer, while the easiest was the client–server.

     

An empirical study of the cross-channel effects between web and mobile shopping channels

With the increasing popularity of mobile applications, increasingly more e-commerce websites are providing mobile shopping services that enable their consumers to access their products and services through an additional mobile channel. The question arises here as to whether the new channel brings new sales or merely shifts consumers from the web to the mobile channel. Using the 2½-year transaction data obtained from an e-commerce company that expanded its web service onto a mobile platform, we investigated the impact of the newly introduced mobile channel on the sales of the incumbent web channel, and whether it could stimulate new consumption from consumers. Our empirical results indicate that after the adoption of the mobile channel, the purchases on the web channel were slightly cannibalized; however, the consumers’ purchases increased overall, suggesting that the positive synergy effect of the new channel overrode the negative cannibalization effect. Our investigation contributes to multichannel e-commerce literature by empirically testing the cross-channel effects of a new mobile channel and also providing insights for e-retailers interested in introducing a new mobile channel.     

Active learning: an empirical study of common baselines

Most of the empirical evaluations of active learning approaches in the literature have focused on a single classifier and a single performance measure. We present an extensive empirical evaluation of common active learning baselines using two probabilistic classifiers and several performance measures on a number of large datasets. In addition to providing important practical advice, our findings highlight the importance of overlooked choices in active learning experiments in the literature. For example, one of our findings shows that model selection is as important as devising an active learning approach, and choosing one classifier and one performance measure can often lead to unexpected and unwarranted conclusions. Active learning should generally improve the model’s capability to distinguish between instances of different classes, but our findings show that the improvements provided by active learning for one performance measure often came at the expense of another measure. We present several such results, raise questions, guide users and researchers to better alternatives, caution against unforeseen side effects of active learning, and suggest future research directions.     

Encouraging behavioral diversity in evolutionary robotics: an empirical study

Evolutionary robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task-and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation; however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.     

An empirical study examining the perceptions and behaviours of security-conscious users of mobile authentication

The purpose of this study is to better understand, from an explorative qualitative perspective, the motivations and practices of highly security-conscious users of mobile authentication, and their underlying mental models of those behaviours. Mobile authentication studies have largely overlooked the mindset of these users in the upper bound of security experience, who have considered their behaviour in terms of detailed knowledge of mobile authentication risk. Twenty IT professionals who self-identified as security-conscious mobile device users, many with decades of intensive security-specific experience, were interviewed for this study regarding their opinions and experiences with mobile device authentication and security. These users described usability and situational impairment issues, as well as a deep concern for their identity and data security arising from highly contextual combinations of distrust towards underlying technologies and situational risk. Derived implications for development of security methods adapted to these informed perspectives are discussed and will be the basis for follow-on research comparing these findings with everyday users.     

Extracting performance rules of suppliers in the manufacturing industry: an empirical study

Performance evaluation of suppliers is increasingly recognized as a critical indicator in supply chain cooperation. Traditional performance evaluation methods have the problems of a simple buy/sell relation and in one’s subjective views between manufacturers and suppliers, and they lack objective automatic evaluation processes in the supply chain considered. Statistical techniques used for evaluation rely on the restrictive assumptions of linear separability, multivariate normality, and independence of the predictive variables. Unfortunately, many of the common models of performance evaluation of suppliers violate these assumptions. The study proposes an integrated model by combining K-means clustering, feature selection, and the decision tree method into a single evaluation model to assess the performance of suppliers and simultaneously tackles the above-mentioned shortcomings. The integrated model is illustrated with an empirical case study of a manufacturer for an original design manufacturer (ODM) to demonstrate the model performance. The experimental results indicate that the proposed method outperforms listed methods in terms of accuracy, and three redundant attributes can be eliminated from the empirical case. Furthermore, the extracted rules by the decision tree C4.5 algorithm form an automatic knowledge system for supplier performance evaluation.