Chapter 8 Factors influencing the adoption of mobile health apps in the UAE

Increasing technology use has led to an increase in mobile health (mHealth) applications (apps). Users are adopting these applications for many reasons related to acceptance and gamification; however, there still needs to be a consensus on which factors most affect user adoption. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study examines the factors influencing the acceptance of mHealth technology. We collected data from 198 United Arab Emirate users who had previously used mHealth apps to improve their self-healthcare. The findings suggested that the intention of using this technology is positively influenced by: (1) the levels of performance expectancy and facilitating conditions concerning the use, (2) the level of gamification impact, and (3) the degree of personal innovativeness in the simple design of mHealth apps. These findings extend the theoretical concept of the UTAUT model in mobile healthcare technology. The value of the study lies in constructing an integrated digital transformation model that will assist healthcare companies in comprehending the influence of available resources and lead them to invest significantly in the incumbent digital infrastructure. The findings shall help healthcare practitioners identify critical drivers and key challenges faced by stakeholders concerning mobile health app use in an emerging Arab country that has the vision to improve its healthcare through digital transformation

Proof systems in blockchains: A survey

© 2019 IEEE. Blockchain is a prime example of disruptive technology in multiple levels. With the advent of blockchains becomes obsolete the need for a mutually trusted third party acting as intermediary between agents which do not necessarily trust each other in transactions of any kind, including political or shareholder voting, crowdfunding, financial deals, logistics and supply chain management, and contract formulation. An integral part of the blockchain stack is the proof system, namely the mechanism efficiently verifying the claims of various blockchain stakeholders. Thus, trust is effectively established in a literally trustless environment with purely computational means. This is especially critical in the digital formulation of smart contracts where clauses are to be strictly upheld by intelligent agents. The most prominent proof systems recently proposed in the scientific literature are reviewed. Additionally, the applications of blockchain technology to smart contracts is discussed. The latter allows clause re-negotiation, increasing thus the flexibility factor in transactions. As a concrete example, a simple smart contract written in Solidity, a high level language for the Ethereum Virtual Machine, is presented

MyMajor: Assisting IT students with major selection

Information technology (IT) in university education plays a crucial role in preparing students for future technological jobs. University students desiring to select an IT major can access a plethora of on-line information about the majors in the form of university brochures, videos, articles, job prospects, and more. Nevertheless, such information is scattered, not interactive, and often presented without usability in mind, hence students have to spend considerable time and effort to obtain an overview of a desired major. In response, the authors present, MyMajor, a tool that helps IT students to select a major by providing all relevant information in one space: Relevant job demand, job salary distribution, overview of vacancies, notable employers and expert interviews. The tool was designed to pack much information using user-centric visualisations, as well as insights. Preliminarily evaluation of MyMajor shows that students find the tool useful, easy to navigate and understandable

Building Trusted Startup Teams from LinkedIn Attributes: A Higher Order Probabilistic Analysis

© 2020 IEEE. Startups arguably contribute to the current business landscape by developing innovative products and services. The discovery of business partners and employees with a specific background which can be verified stands out repeatedly as a prime obstacle. LinkedIn is a popular platform where professional milestones, endorsements, recommendations, and skills are posted. A graph search algorithm with a BFS and a DFS strategy for seeking trusted candidates in LinkedIn is proposed. Both strategies rely on a metric for assessing the trustworthiness of an account according to LinkedIn attributes. Also, a stochastic vertex selection mechanism reminiscent of preferential attachment guides search. Both strategies were verified against a large segment of the vivid startup ecosystem of Patras, Hellas. A higher order probabilistic analysis suggests that BFS is more suitable. Findings also imply that emphasis should be given to local networking events, peer interaction, and to tasks allowing verifiable credit for the respective work

Engaging Students With a Chatbot-Based Academic Advising System

Advising systems automate aspects of academic advising. Traditionally, advising systems focused on specialized tasks such as course selection. Recently, chatbot-based advising systems have emerged as they emulate scenario-based advising. Nevertheless, the design of most chatbot-based advising systems is not user-centric, potentially causing a lack of adoption. Further, there is a lack of studies reporting findings of usability evaluation of chatbot-based advising systems. In response, we contribute a chatbot-based academic advising system, MyAdvisor, that helps students with prescriptive academic inquiries. The system is based on real advising scenarios and designed with usability principles. The results show that students learn the system fast and find it helpful. This work contributes (1) scenario-based functional requirements and usability requirements of the chatbot-based advising system, (2) the application of usability heuristics in the design of the system, and (3) findings of empirically evaluating the system usability

Physical activity recognizer based on multimodal sensors in smartphone for ubiquitous-lifecare services

© 2017 IEEE. Smartphone-based activity recognition is an emerging field of research that enables a large number of human-centric applications in the u-lifecare domain. Currently, major challenges include the development of real-time position independent and lightweight classifier models to recognize the physical activities inside the smartphone environment. In this paper, we propose a real-time position independent physical activity recognizer that utilizes the embedded accelerometer, ambient light and proximity sensors of smartphone to recognize the physical activities. To validate our model, we implement it in an open source Android platform to recognize six physical activities and performed extensive experiments over 10 subjects. We obtained 88% of class-accuracy and 91.55% F-measures. It is expected that our model would be a practical and realistic solution for physical activity recognition due to its unobtrusive nature and real-time classification of activities

Understanding Influencers of College Major Decision: The UAE Case

This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other students in their major decision. The results show that financial factors such as income and business opportunities related to the major are crucial. Further, gender suitability for the job and passion are influential. Students conduct internet searches, use social media, and read brochures in the process of major decisions. Moreover, students think job alignment with the UAE vision and information related to job availability, income, and skills are critical for future students to decide on their major. Finally, STEM students are more influenced by business opportunities, prestige, and career advancement than others