The effects of mental simulations,innovativeness on intention to adopt brand application

This study explores three important factors—mental simulation, innovativeness, and need for cognition—to determine the effects of these factors on the perception of newly developed branded applications. The study identifies two types of mental simulation (process-focused and outcome-focused), innovativeness (early and later adopters) and need for cognition (low and high) to inform items used to survey respondents about their willingness to adopt new branded apps and their attitudes towards the focal brands.The study demonstrates the effects of mental simulation, innovativeness, and need for cognition on adoption of the branded app. This experiment improves our understanding of how three-way interaction between the abovementioned factors affects the intention to adopt a new branded app. The results of a MANOVA indicated statistically significant effects of the abovementioned three-way interaction on the dependent variables. It is anticipated that the analysis resulting from this valuable study will provide a point of reference for further empirical and theoretical studies on the development of information technology products and services.     

“Masculine Guys Only”: The effects of femmephobic mobile dating application profiles on partner selection for men who have sex with men

Mobile dating applications (apps) have changed the way gay men find others in their geographic area for sexual activity and romantic relationships. Many of these apps are branded in relation to traditional masculinity and have become a breeding ground for femmephobic, or anti-effeminate, language. Past research has not examined the effects of femmephobic language in social networking apps designed for men who have sex with men (MSM) on app users' perceptions. This research employed an online experiment of 143 MSM app users to test how users respond to femmephobic and non-femmephobic language use in MSM dating profiles. Participants rated the profile users, as well as reported their desire to meet the user in an offline context. Results indicated that the use of femmephobic language in dating profiles affects a potential partner's perceived intelligence, sexual confidence, and dateability, as well as one's desire to meet potential partners offline for friendship or romantic purposes. Anti-effeminacy was an important moderator of the main effect.     

Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.

     

Brand interactions and social media: Enhancing user loyalty through social networking sites

This paper aims to investigate how user loyalty can be achieved and maintained through social networking sites. More specifically, we intend to test the relationships between brands, user loyalty and social media. The research thus provides insights into user-brand relationships through social media and argues how loyal customers can be through social networking websites. Although there are considerable numbers of studies about loyalty; there exists very limited work studying user loyalty through social networking websites. This research presents clearly the reasons for engaging with brands online and examines user behaviors and loyalty. Research provided strong evidence that majority of the social network users follow brand fan pages via social media, even though they have different reasons to do so. The study also measures users’ behavioral and attitudinal loyalty behaviors. Their level of trust to the information they obtained about brands through social media is also established. The hypotheses tested show that brands and customer satisfaction are both positively related to users’ behavioral loyalty.     

The relationship between perceived e-service quality and brand equity: A simultaneous equations system approach

Using the data collected from the online banking users in Taiwan, we build a hierarchical model of e-banking service quality and investigate the relationships among e-service quality, trust, satisfaction, loyalty, and brand equity. Moreover, the simultaneous equations system approach is also applied to transfer the traditional satisfaction-loyalty path into the simultaneous relationships between trust and loyalty, and between satisfaction and loyalty. As the structural form coefficients demonstrate the direct relations between our research constructs, the reduced form estimates further disclose the total impacts of the quality of e-banking service on trust, satisfaction, loyalty, and brand equity, respectively. The results indicate that the perceived quality formed through interaction with an online banking service positively affects customer trust and satisfaction, which in turn influence loyalty and brand equity. The significant simultaneous relationships between trust and loyalty, and between satisfaction and loyalty, are confirmed by our data, implying that these relationships must be determined simultaneously, rather than sequentially.     

The effects of social media based brand communities on brand community markers,value creation practices,brand trust and brand loyalty

Social media based brand communities are communities initiated on the platform of social media. In this article, we explore whether brand communities based on social media (a special type of online brand communities) have positive effects on the main community elements and value creation practices in the communities as well as on brand trust and brand loyalty. A survey based empirical study with 441 respondents was conducted. The results of structural equation modeling show that brand communities established on social media have positive effects on community markers (i.e., shared consciousness, shared rituals and traditions, and obligations to society), which have positive effects on value creation practices (i.e., social networking, community engagement, impressions management, and brand use). Such communities could enhance brand loyalty through brand use and impression management practices. We show that brand trust has a full mediating role in converting value creation practices into brand loyalty. Implications for practice and future research opportunities are discussed.     

Role of social media community in strengthening trust and loyalty for a university

As the increasing internationalization of higher education creates competition among the universities, many institutions are making use of social media communities to attract and retain students. We surveyed 206 users of a university Facebook page to investigate how it may be related to their identification with the university community and the university brand. We further investigate how this identification is associated with the trust and loyalty to the institution and is likely to be linked to positive behaviors such as promoting the university to their peers as an active member of the university community. Our results show that the perceived strength of the Facebook community is related to the identification with the university community and the university brand which in turn are related to trust and loyalty. Higher loyalty is linked to citizenship behaviors such as increased contribution to the educational process labeled in-role behaviors and providing positive word of mouth about the university labeled extra-role behaviors. With the challenges of a diverse and global student body which is expected to continue increasing, universities can benefit from use of social media in developing identity, loyalty and trust.     

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the overall impression of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 79% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by developers for the different platforms that they wish to support.     

Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study

The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone user's personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a machine-learning model is developed to automatically determine a user's personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app.     

Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mobile app reviews by users contain a wealth of information on the issues that users are experiencing. For example, a review might contain a feature request, a bug report, and/or a privacy complaint. Developers, users and app store owners (e.g. Apple, Blackberry, Google, Microsoft) can benefit from a better understanding of these issues – developers can better understand users’ concerns, app store owners can spot anomalous apps, and users can compare similar apps to decide which ones to download or purchase. However, user reviews are not labelled, e.g. we do not know which types of issues are raised in a review. Hence, one must sift through potentially thousands of reviews with slang and abbreviations to understand the various types of issues. Moreover, the unstructured and informal nature of reviews complicates the automated labelling of such reviews. In this paper, we study the multi-labelled nature of reviews from 20 mobile apps in the Google Play Store and Apple App Store. We find that up to 30 % of the reviews raise various types of issues in a single review (e.g. a review might contain a feature request and a bug report). We then propose an approach that can automatically assign multiple labels to reviews based on the raised issues with a precision of 66 % and recall of 65 %. Finally, we apply our approach to address three proof-of-concept analytics use case scenarios: (i) we compare competing apps to assist developers and users, (ii) we provide an overview of 601,221 reviews from 12,000 apps in the Google Play Store to assist app store owners and developers and (iii) we detect anomalous apps in the Google Play Store to assist app store owners and users.     

Testing an extended model of consumer behavior in the context of social media-based brand communities

Due to the benefits of brand community initiatives and the potential advantages of social media, many marketing managers made significant investments in building brand communities based on social media. A noticeable issue is how to measure the success of such investments in ways that are understandable and comparable to other marketing initiatives. To address this issue, it is essential to understand how social media-based brand communities influence customers' perceptions and behaviors. The purpose of this article is to develop and estimate an extended model of how different aspects of customers' relationships with such communities influence their perceptions and behaviors. The paper describes a way a customer's identification with a brand influences relationships with brand community elements, brand community markers, and ultimately brand relationship quality and brand loyalty. Then using a large sample of brand community members in social media and structural equation modeling, the authors estimate how social media-based brand communities influence brand relationship quality and brand loyalty. Managerial implications are discussed.     

Exploring privacy paradox in information-sensitive mobile app adoption: A cross-cultural comparison

This paper proposes and tests a conceptual model of private-information sensitive mobile app adoption utilizing privacy calculus approach. It also explores the role of personality in affecting perceived benefits of using mobile apps and compares the findings across two countries: the US and China. Irrespective of the cultural environment, millennial mobile app users download apps that require access to sensitive personal information in order to satisfy their informational and social (but not entertainment) needs. Perceived privacy concern does not influence adoption or future use of private-information sensitive apps. Extraversion and agreeableness are positively related to user perceptions of benefits obtained from using apps.     

Why do young people use fitness apps? Cognitive characteristics and app quality

This study examined the effects of young adults’ social-cognitive characteristics and fitness apps’ quality-related characteristics on users’ intention to continue using apps. This study used a self-report questionnaire to collect data from 201 participants between November 29 and December 16, 2016. PASW/WIN 20.0 was used to perform Pearson’s correlation analysis, and hierarchical multiple regression. Results showed that users’ social-cognitive characteristics and the app’ quality-related characteristics accounted for 39.3 and 1.6% of users’ intention to continue using fitness apps, respectively. Social-cognitive characteristics included quality-related app characteristics, which explained 40.9% of users’ intention to continue using the apps. Self-efficacy, innovative propensity, outcome expectations, and engagement were key variables affecting the intention to continue using apps. Therefore, it is suggested that researchers or healthcare providers who want to utilize fitness apps for young adults could invest time and effort in the selection of existing high-quality apps and design intervention programs to stimulate users’ social-cognitive factors.     

Effects of the aesthetic design of icons on app downloads: evidence from an android market

With the rapid development of the mobile app market, understanding the determinants of mobile app success has become vital to researchers and mobile app developers. Extant research on mobile applications primarily focused on the numerical and textual attributes of apps. Minimal attention has been provided to how the visual attributes of apps affect the download behavior of users. Among the features of app “appearance”, this study focuses on the effects of app icon on demand. With aesthetic product and interface design theories, we analyze icons from three aspects, namely, color, complexity, and symmetry, through image processing. Using a dataset collected from one of the largest Chinese Android websites, we find that icon appearance influences the download behavior of users. Particularly, apps with icons featuring higher colorfulness, proper complexity, and slight asymmetry lead to more downloads. These findings can help developers design their apps.     

Effects of the design of mobile security notifications and mobile app usability on users’ security perceptions and continued use intention

The explosive global adoption of mobile applications (i.e., apps) has been fraught with security and privacy issues. App users typically have a poor understanding of information security; worse, they routinely ignore security notifications designed to increase security on apps. By considering both mobile app interface usability and mobile security notification (MSN) design, we investigate how security perceptions of apps are formed and how these perceptions influence users’ intentions to continue using apps. Accordingly, we designed and conducted a set of controlled survey experiments with 317 participants in different MSN interface scenarios by manipulating the types of MSN interfaces (i.e., high vs. low disruption), the context (hedonic vs. utilitarian scenarios), and the degree of MSN intrusiveness (high vs. low intrusiveness). We found that both app interface usability and the design of MSNs significantly impacted users’ perceived security, which, in turn, has a positive influence on users’ intention to continue using the app. In addition, we identified an important conundrum: disruptive MSNs—a common approach to delivering MSNs—irritate users and negatively influence their perceptions of app security. Thus, our results directly challenge current practice. If these results hold, current practice should shift away from MSNs that interrupt task performance.     

融合主题模型和协同过滤的多样化移动应用推荐

随着移动应用的急速增长,手机助手等移动应用获取平台也面临着信息过载的问题.面对大量的移动应用,用户很难找到想到的或适合的应用,而另一方面长尾应用淹没在资源池中不被人所知.已有推荐方法多注重推荐准确率,忽视多样性,推荐结果中多是下载量高的应用,使得推荐系统的数据积累越来越偏向于热门应用,导致长期的推荐效果越来越差.针对此问题,本文首先改进了两个推荐方法,提出了将用户的主题模型和应用的主题模型与MF相结合的LDA_MF模型,以及将应用的标签信息和用户行为数据同时加以考虑的LDA_CF算法.为了结合不同算法的优点,在保证推荐准确率的条件下提升推荐结果的多样性,我们提出了融合LDA_MF、LDA_CF以及经典的基于物品的协同过滤模型的混合推荐算法.文章使用真实的大数据评测所提推荐算法,结果显示所提推荐方法能够得到推荐多样性更好且准确率高的结果.     

Product‐to‐Service Extension: The Impact of Brand Equity on Upscaled Service

A recent trend in the luxury industry shows many companies using brand extension strategies to leverage their assets among competitors. Despite the popularity of brand extensions, limited research has been conducted to determine its effectiveness when using parent product brands in order to introduce new service brands. Thus, the primary purpose of this study is to propose the framework to increase the understanding of luxury brand extensions by focusing on the horizontal category brand extension from product to service. The impact of perceived quality, brand association and brand loyalty of the luxury car brand on consumers’ perception and attitude toward the rental car service brand was observed to differentiate from other research that focuses on brand extension within product categories. A total of 324 samples were collected and analyzed using structural equation modeling with AMOS. The brand association of the parent brand showed a significant impact on the evaluation of the extended service brand. In addition, a high level of the brand association was also found to influence the final purchase decision of the extended service brand. However, this study could not find any significant effect of perceived quality and brand loyalty for the parent product brand on the extended service brand. The framework proposed in this study has merit to increase the understanding of service brand extensions by exploring luxury car brands and luxury rental car brands. In the service brand extension, there was a gap in consumer perception between the product brand and the extended service brand. In order to maximize the positive impact of the parent brand, marketers and retailers should investigate the different roles of the brand equity items.     

数据驱动的移动应用用户接受度建模与预测

应用市场（app market）已经成为互联网环境下软件应用开发和交付的一种主流模式.相对于传统模式,应用市场模式下,软件的交付周期更短,用户的反馈更快,最终用户和开发者之间的联系更加紧密和直接.为应对激烈的竞争和动态演变的用户需求,移动应用开发者必须以快速迭代的方式不断更新应用,修复错误缺陷,完善应用质量,提升用户体验.因此,如何正确和综合理解用户对软件的接受程度（简称用户接受度）,是应用市场模式下软件开发需考量的重要因素.近年来兴起的软件解析学（software analytics）关注大数据分析技术在软件行业中的具体应用,对软件生命周期中大规模、多种类的相关数据进行挖掘和分析,被认为是帮助开发者提取有效信息、作出正确决策的有效途径.从软件解析学的角度,首先论证了为移动应用构建综合的用户接受度指标模型的必要性和可行性,并从用户评价数据、操作数据、交互行为数据这3个维度给出基本的用户接受度指标.在此基础上,使用大规模真实数据集,在目标用户群体预测、用户规模预测和更新效果预测等典型的用户接受度指标预测问题中,结合具体指标,提取移动应用生命周期不同阶段的重要特征,以协同过滤、回归融合、概率模型等方法验证用户接受度的可预测性,并讨论了预测结果与特征在移动应用开发过程中可能提供的指导.     

Forensic taxonomy of android productivity apps

Android productivity apps have provided the facility of having a constantly accessible and productive workforce to the information and work capabilities needed by the users. With hundreds of productivity apps available in the Android app market, it is necessary to develop a taxonomy for the forensic investigators and the end users to allow them to know what personal data remnants are available from the productivity apps. In this paper, 30 popular Android productivity apps were examined. A logical extraction of the Android phone was collected by using a well-known mobile forensic tool- XRY to extract various information of forensic interest such as user email ID and list of tasks. Based on the findings, a two-dimensional taxonomy of the forensic artefacts of the productivity apps is proposed with the app categories in one dimension and the classes of artefacts in the other dimension. The artefacts identified in the study of the apps are summarised using the taxonomy. In addition, a comparison with the existing forensic taxonomies of different categories of Android apps is provided to facilitate timely collection and analysis of evidentiary materials from mobile devices.