Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications

Due to the popularity of smart mobile phones and context-aware technology, various contextual data relevant to users' diverse activities with mobile phones is available around us. This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users. Based on the availability of mobile phone data, and the usefulness of data-driven applications, in this paper, we discuss about mobile data science that involves in collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, in order to make dynamic decisions intelligently in various day-to-day situations of the users. For this, we first discuss the fundamental concepts and the potentiality of mobile data science to build intelligent applications. We also highlight the key elements and explain various key modules involving in the process of mobile data science. This article is the first in the field to draw a big picture, and thinking about mobile data science, and it's potentiality in developing various data-driven intelligent mobile applications. We believe this study will help both the researchers and application developers for building smart data-driven mobile applications, to assist the end mobile phone users in their daily activities.Comment: Journal, 11 pages, Double Colum

Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing

This contribution reviews the vast scope of digital application areas, which shape the digital marketing landscape and coin the present term “marketing intelligence” from a marketing technique point of view. Additionally, marketing intelligence as social engineering techniques are described. The review ranges from digital IT- and big data marketing until marketing 5.0 as digitalized trust marketing. The multiplicity of applications and interdependencies of the digital and social techniques reviewed should show that big data and marketing intelligence have already become a marketing reality. It becomes clear that marketing is witnessing a methodological, technical and cultural paradigm shift that augments and amplifies traditional outbound marketing with inbound marketing

Evaluating the Persuasiveness of Mobile Health: The Intersection of Persuasive System Design and Data Science

Persuasive technology is an umbrella term that encompasses any software (e.g., mobile app) or hardware (e.g., smartwatch) designed to influence users to perform a preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. This research examines the roles psychological characteristics play in interpreted mHealth screen perceived persuasiveness. A review of the literature revealed a gap regarding how developers of digital health technologies are often tasked with developing tools designed to engage patients, yet little emphasis has been placed on understanding what psychological characteristics motivate and demotivate their users to engage with digital health technologies. Developers must move past using a cookie-cutter, one size fits all solution, and seek to develop digital health technologies designed to traverse the terrain that navigates between the fluid nature of goals and user preferences. This terrain is often determined by user’s psychological characteristics and demographic (control) variables. An experiment was designed to evaluate how psychological characteristics (self-efficacy, xiv health consciousness, health motivation, and the Big Five personality traits) impact the perceived persuasiveness of digital health technologies utilizing the Persuasive System Design (PSD) framework. This study used multiple linear regressions and Contrast, a publicly available Python implementation of the contrast pattern mining algorithm Search and Testing for Understandable Consistent Contrasts (STUCCO), to study the multifaceted needs of the users of digital health technologies based on psychological characteristics. The results of this experiment show psychological characteristics (selfefficacy, health consciousness, health motivation, and extraversion) enhancing the perceived persuasiveness of digital health technologies. The findings of the study revealed that screens utilizing techniques for the primary task support have high perceived persuasiveness scores. System credibility techniques were found to be a contributor to perceived persuasiveness and should be used in the development of persuasive technologies. The results of this study show practitioners should abstain from using social support techniques. Persuasive techniques from the social support category were found to have very low perceived persuasive scores which indicate a lower ability to persuade mHealth app users to utilize the tool. The findings strongly suggest the distribution of perceived persuasiveness shifts from negatively skewed to positively skewed as participants get older. Additionally, this shift occurs earlier in females (i.e., in the 40-59 age group) compared to males who do not shift until the oldest age group (i.e., in the 60 and older age group). The results imply that an individual user’s psychological characteristics affect interpreted mHealth screen perceived persuasiveness, and that combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness

Privacy as a Public Good

Privacy is commonly studied as a private good: my personal data is mine to protect and control, and yours is yours. This conception of privacy misses an important component of the policy problem. An individual who is careless with data exposes not only extensive information about herself, but about others as well. The negative externalities imposed on nonconsenting outsiders by such carelessness can be productively studied in terms of welfare economics. If all relevant individuals maximize private benefit, and expect all other relevant individuals to do the same, neoclassical economic theory predicts that society will achieve a suboptimal level of privacy. This prediction holds even if all individuals cherish privacy with the same intensity. As the theoretical literature would have it, the struggle for privacy is destined to become a tragedy. But according to the experimental public-goods literature, there is hope. Like in real life, people in experiments cooperate in groups at rates well above those predicted by neoclassical theory. Groups can be aided in their struggle to produce public goods by institutions, such as communication, framing, or sanction. With these institutions, communities can manage public goods without heavy-handed government intervention. Legal scholarship has not fully engaged this problem in these terms. In this Article, we explain why privacy has aspects of a public good, and we draw lessons from both the theoretical and the empirical literature on public goods to inform the policy discourse on privacy